vecId stringlengths 12 23 | id stringlengths 2 13 | conference stringclasses 11
values | year float64 2.02k 2.03k | title stringlengths 6 189 | abstract stringlengths 10 4.74k | author stringlengths 0 7.45k | aff stringlengths 0 7.16k | status stringclasses 11
values | track stringclasses 4
values | keywords stringlengths 0 804 | github stringlengths 0 141 | site stringlengths 0 193 | gsCitation float64 -1 11.1k | arxiv stringlengths 0 12 | text stringlengths 58 4.82k | vector list |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
wacv_2025_b0afe02e9a | b0afe02e9a | wacv | 2,025 | Improving Shift Invariance in Convolutional Neural Networks with Translation Invariant Polyphase Sampling | Convolutional neural networks (CNNs) widely deployed in several applications contain downsampling operators in their pooling layers which have been observed to be sensitive to pixel-level shift affecting the robustness of CNNs. We study shift invariance through the lens of maximum sampling bias (MSB) and find MSB to be... | Sourajit Saha; Tejas Gokhale | University of Maryland, Baltimore County; University of Maryland, Baltimore County | Poster | main | https://github.com/sourajitcs/tips/ | https://openaccess.thecvf.com/content/WACV2025/html/Saha_Improving_Shift_Invariance_in_Convolutional_Neural_Networks_with_Translation_Invariant_WACV_2025_paper.html | 2 | 2404.07410 | Improving Shift Invariance in Convolutional Neural Networks with Translation Invariant Polyphase Sampling
Convolutional neural networks (CNNs) widely deployed in several applications contain downsampling operators in their pooling layers which have been observed to be sensitive to pixel-level shift affecting the robust... | [
-0.04565765708684921,
-0.034463103860616684,
-0.04448506981134415,
0.0511908084154129,
0.0220226738601923,
-0.042103249579668045,
0.01601315848529339,
0.003538376884534955,
-0.0002613703836686909,
0.053096264600753784,
-0.027848972007632256,
-0.005491927266120911,
-0.005423220805823803,
0.... | |
wacv_2025_34042de147 | 34042de147 | wacv | 2,025 | Improving Uncertainty Estimation with Confidence-Aware Training Data | AI-driven second-opinion systems play a crucial role in decision-making especially in medicine where accurate predictions guide clinicians. However quantifying uncertainty in deep learning is challenging as current methods often rely on hard class labels which do not reflect true prediction confidence. This often resul... | Sergey Korchagin; Ekaterina Zaychenkova; Aleksei Khalin; Aleksandr Yugay; Alexey Zaytsev; Egor Ershov | IITP RAS, Russia; IITP RAS, Russia; IITP RAS, Russia; Skoltech, Russia; Skoltech + Sber, Russia; IITP RAS + AIRI, Russia | Poster | main | https://github.com/createcolor/confidence-aware-uncertainty | https://openaccess.thecvf.com/content/WACV2025/html/Korchagin_Improving_Uncertainty_Estimation_with_Confidence-Aware_Training_Data_WACV_2025_paper.html | 0 | Improving Uncertainty Estimation with Confidence-Aware Training Data
AI-driven second-opinion systems play a crucial role in decision-making especially in medicine where accurate predictions guide clinicians. However quantifying uncertainty in deep learning is challenging as current methods often rely on hard class lab... | [
-0.07728421688079834,
-0.05128026008605957,
-0.015682797878980637,
0.012580706737935543,
-0.002812467748299241,
-0.028550736606121063,
0.014773234724998474,
0.03820168972015381,
0.02221251092851162,
0.019608285278081894,
-0.008640858344733715,
-0.005112707149237394,
0.0002453429333399981,
... | ||
wacv_2025_4202c5a497 | 4202c5a497 | wacv | 2,025 | Improving Zero-Shot Object-Level Change Detection by Incorporating Visual Correspondence | Detecting object-level changes between two images across possibly different views (Fig. 1) is a core task in many applications that involve visual inspection or camera surveillance. Existing change-detection approaches suffer from three major limitations: (1) lack of evaluation on image pairs that contain no changes le... | Hung Huy Nguyen; Pooyan Rahmanzadehgervi; Long Mai; Anh Totti Nguyen | Auburn University; Auburn University; Adobe Research; Auburn University | Poster | main | https://openaccess.thecvf.com/content/WACV2025/html/Nguyen_Improving_Zero-Shot_Object-Level_Change_Detection_by_Incorporating_Visual_Correspondence_WACV_2025_paper.html | 1 | 2501.05555 | Improving Zero-Shot Object-Level Change Detection by Incorporating Visual Correspondence
Detecting object-level changes between two images across possibly different views (Fig. 1) is a core task in many applications that involve visual inspection or camera surveillance. Existing change-detection approaches suffer from ... | [
-0.08880702406167984,
-0.02535770833492279,
-0.029119163751602173,
0.02651366777718067,
0.019357729703187943,
-0.019669655710458755,
0.011963260360062122,
0.030623745173215866,
0.01042198110371828,
-0.008160521276295185,
-0.03271548077464104,
-0.01823846809566021,
0.0016009117243811488,
0.... | ||
wacv_2025_6a065ce353 | 6a065ce353 | wacv | 2,025 | InDistill: Information Flow-Preserving Knowledge Distillation for Model Compression | In this paper we introduce InDistill a method that serves as a warmup stage for enhancing Knowledge Distillation (KD) effectiveness. InDistill focuses on transferring critical information flow paths from a heavyweight teacher to a lightweight student. This is achieved via a training scheme based on curriculum learning ... | Ioannis Sarridis; Christos Koutlis; Giorgos Kordopatis-Zilos; Yiannis Kompatsiaris; Symeon Papadopoulos | Information Technologies Institute, CERTH; Information Technologies Institute, CERTH; VRG, FEE, Czech Technical University in Prague; Information Technologies Institute, CERTH; Information Technologies Institute, CERTH | Poster | main | https://github.com/gsarridis/InDistill | https://openaccess.thecvf.com/content/WACV2025/html/Sarridis_InDistill_Information_Flow-Preserving_Knowledge_Distillation_for_Model_Compression_WACV_2025_paper.html | 4 | 2205.10003 | InDistill: Information Flow-Preserving Knowledge Distillation for Model Compression
In this paper we introduce InDistill a method that serves as a warmup stage for enhancing Knowledge Distillation (KD) effectiveness. InDistill focuses on transferring critical information flow paths from a heavyweight teacher to a light... | [
-0.04960639774799347,
-0.011047190055251122,
-0.028027983382344246,
0.0057631502859294415,
-0.023900259286165237,
-0.04440988972783089,
0.006491030100733042,
0.04227231815457344,
-0.01201462559401989,
0.03344562277197838,
0.0035150148905813694,
-0.012291035614907742,
-0.004178399220108986,
... | |
wacv_2025_6b73234651 | 6b73234651 | wacv | 2,025 | Incorporating Task Progress Knowledge for Subgoal Generation in Robotic Manipulation through Image Edits | Understanding the progress of a task allows humans to not only track what has been done but also to better plan for future goals. We demonstrate TaKSIE a novel framework that incorporates task progress knowledge into visual subgoal generation for robotic manipulation tasks. We jointly train a recurrent network with a l... | Xuhui Kang; Yen-Ling Kuo | University of Virginia; University of Virginia | Poster | main | https://openaccess.thecvf.com/content/WACV2025/html/Kang_Incorporating_Task_Progress_Knowledge_for_Subgoal_Generation_in_Robotic_Manipulation_WACV_2025_paper.html | 4 | 2410.11013 | Incorporating Task Progress Knowledge for Subgoal Generation in Robotic Manipulation through Image Edits
Understanding the progress of a task allows humans to not only track what has been done but also to better plan for future goals. We demonstrate TaKSIE a novel framework that incorporates task progress knowledge int... | [
-0.04826917499303818,
-0.04512038081884384,
-0.009881659410893917,
0.04093433544039726,
-0.008793473243713379,
-0.0055289133451879025,
0.023615961894392967,
-0.009261162020266056,
0.0032298301812261343,
0.05682648718357086,
-0.01540131215006113,
-0.03535911440849304,
0.00714035565033555,
0... | ||
wacv_2025_267ad2774f | 267ad2774f | wacv | 2,025 | Infant Action Generative Modeling | Despite advancements in human motion generation models their performance drops in infant motion generation due to limited data available and lack of 3D skeleton ground truth. To address this we introduce the infant action generation and classification (InfAGenC) pipeline which combines a transformer-based variational a... | Xiaofei Huang; Elaheh Hatamimajoumerd; Amal Mathew; Sarah Ostadabbas | Augmented Cognition Lab (ACLab), Department of Electrical and Computer Engineering, Northeastern University, Boston, MA, USA; Augmented Cognition Lab (ACLab), Department of Electrical and Computer Engineering, Northeastern University, Boston, MA, USA; Augmented Cognition Lab (ACLab), Department of Electrical and Comput... | Poster | main | https://github.com/ostadabbas/Infant-Action-Generative-Modeling | https://openaccess.thecvf.com/content/WACV2025/html/Huang_Infant_Action_Generative_Modeling_WACV_2025_paper.html | 0 | Infant Action Generative Modeling
Despite advancements in human motion generation models their performance drops in infant motion generation due to limited data available and lack of 3D skeleton ground truth. To address this we introduce the infant action generation and classification (InfAGenC) pipeline which combines... | [
-0.05872448533773422,
-0.03638200834393501,
-0.03943900391459465,
0.009689917787909508,
0.01830422692000866,
-0.06997120380401611,
-0.008246337063610554,
0.05638456344604492,
-0.020210130140185356,
0.039363522082567215,
-0.02953207679092884,
-0.024210641160607338,
-0.027248766273260117,
-0... | ||
wacv_2025_ebfa2da40d | ebfa2da40d | wacv | 2,025 | Inferring Past Human Actions in Homes with Abductive Reasoning | Abductive reasoning aims to make the most likely inference for a given set of incomplete observations. In this paper we introduce "Abductive Past Action Inference" a novel research task aimed at identifying the past actions performed by individuals within homes to reach specific states captured in a single image using ... | Clement Tan; Chai Kiat Yeo; Cheston Tan; Basura Fernando | Nanyang Technological University, Singapore; Nanyang Technological University, Singapore; Centre for Frontier AI Research (CFAR), Agency for Science, Technology and Research (A*STAR); Nanyang Technological University, Singapore+Centre for Frontier AI Research (CFAR), Agency for Science, Technology and Research (A*STAR) | Poster | main | https://github.com/LUNAProject22/AAR | https://openaccess.thecvf.com/content/WACV2025/html/Tan_Inferring_Past_Human_Actions_in_Homes_with_Abductive_Reasoning_WACV_2025_paper.html | 0 | 2210.13984 | Inferring Past Human Actions in Homes with Abductive Reasoning
Abductive reasoning aims to make the most likely inference for a given set of incomplete observations. In this paper we introduce "Abductive Past Action Inference" a novel research task aimed at identifying the past actions performed by individuals within h... | [
-0.03329997509717941,
-0.03486858680844307,
-0.052803702652454376,
0.04320302978157997,
-0.011490569449961185,
-0.03312988206744194,
0.026401851326227188,
-0.011150388047099113,
-0.03424492105841637,
0.026231762021780014,
-0.03866728022694588,
0.007517062593251467,
-0.02953908033668995,
-0... | |
wacv_2025_6990622fe0 | 6990622fe0 | wacv | 2,025 | Information Extraction from Heterogeneous Documents without Ground Truth Labels using Synthetic Label Generation and Knowledge Distillation | Invoices and receipts submitted by employees are visually rich documents (VRDs) with textual visual and layout information. To protect against the risk of fraud and abuse it is crucial for organizations to efficiently extract desired information from submitted receipts. This helps in the assessment of key factors such ... | Aniket Bhattacharyya; Anurag Tripathi | Amazon; Amazon | Poster | main | https://openaccess.thecvf.com/content/WACV2025/html/Bhattacharyya_Information_Extraction_from_Heterogeneous_Documents_without_Ground_Truth_Labels_using_WACV_2025_paper.html | 2 | 2411.14957 | Information Extraction from Heterogeneous Documents without Ground Truth Labels using Synthetic Label Generation and Knowledge Distillation
Invoices and receipts submitted by employees are visually rich documents (VRDs) with textual visual and layout information. To protect against the risk of fraud and abuse it is cru... | [
-0.06732700765132904,
-0.00491277314722538,
0.004851890727877617,
0.023809855803847313,
-0.0071420203894376755,
-0.041400302201509476,
-0.0022901298943907022,
0.021093547344207764,
0.0029200324788689613,
-0.00744643434882164,
-0.01852710358798504,
-0.01487413514405489,
-0.02557077445089817,
... | ||
wacv_2025_74cd8536a8 | 74cd8536a8 | wacv | 2,025 | Information Theoretic Pruning of Coupled Channels in Deep Neural Networks | Variational channel pruning approaches have obtained impressive results thanks to their stochastic nature well established foundation in information theory and the practically appealing structured sparsity pattern they offer. Despite their success in pruning Plain Networks (PlainNets) their application has faced certai... | Peyman Rostami; Nilotpal Sinha; Nidhaleddine Chenni; Anis Kacem; Abd El Rahman Shabayek; Carl Shneider; Djamila Aouada | SnT, University of Luxembourg; SnT, University of Luxembourg; SnT, University of Luxembourg; SnT, University of Luxembourg; SnT, University of Luxembourg; SnT, University of Luxembourg; SnT, University of Luxembourg | Poster | main | https://openaccess.thecvf.com/content/WACV2025/html/Rostami_Information_Theoretic_Pruning_of_Coupled_Channels_in_Deep_Neural_Networks_WACV_2025_paper.html | 0 | Information Theoretic Pruning of Coupled Channels in Deep Neural Networks
Variational channel pruning approaches have obtained impressive results thanks to their stochastic nature well established foundation in information theory and the practically appealing structured sparsity pattern they offer. Despite their succes... | [
-0.03890588507056236,
-0.009511573240160942,
-0.009268645197153091,
0.05000583454966545,
-0.011725956574082375,
-0.01620144210755825,
0.04888462647795677,
-0.006830019410699606,
-0.0441008098423481,
0.049856338649988174,
-0.051426030695438385,
-0.044325049966573715,
-0.03159935027360916,
0... | |||
wacv_2025_d2c15f6e92 | d2c15f6e92 | wacv | 2,025 | Instance-Warp: Saliency Guided Image Warping for Unsupervised Domain Adaptation | Driving is challenging in conditions like night rain and snow. Lack of good labeled datasets has hampered progress in scene understanding under such conditions. Unsupervised Domain Adaptation (UDA) using large labeled clear-day datasets is a promising research direction in such cases. However many UDA methods are train... | Shen Zheng; Anurag Ghosh; Srinivasa Narasimhan | Carnegie Mellon University; Carnegie Mellon University; Carnegie Mellon University | Poster | main | https://github.com/ShenZheng2000/Instance-Warp | https://openaccess.thecvf.com/content/WACV2025/html/Zheng_Instance-Warp_Saliency_Guided_Image_Warping_for_Unsupervised_Domain_Adaptation_WACV_2025_paper.html | 0 | Instance-Warp: Saliency Guided Image Warping for Unsupervised Domain Adaptation
Driving is challenging in conditions like night rain and snow. Lack of good labeled datasets has hampered progress in scene understanding under such conditions. Unsupervised Domain Adaptation (UDA) using large labeled clear-day datasets is ... | [
-0.045987363904714584,
-0.002130639273673296,
-0.04513091966509819,
-0.007368218153715134,
-0.02323572151362896,
-0.010677632875740528,
0.04781196266412735,
0.04568947106599808,
0.017910867929458618,
0.005343471188098192,
-0.013591406866908073,
-0.015611499547958374,
0.006395408883690834,
... | ||
wacv_2025_1d3a908e1b | 1d3a908e1b | wacv | 2,025 | Instructive3D: Editing Large Reconstruction Models with Text Instructions | Transformer based methods have enabled users to create modify and comprehend text and image data. Recently proposed Large Reconstruction Models (LRMs) further extend this by providing the ability to generate high-quality 3D models with the help of a single object image. These models however lack the ability to manipula... | Kunal Kathare; Ankit Dhiman; K Vikas Gowda; Siddharth Aravindan; Shubham Monga; Basavaraja Shanthappa Vandrotti; Lokesh R Boregowda | ;;;;;; | Poster | main | https://openaccess.thecvf.com/content/WACV2025/html/Kathare_Instructive3D_Editing_Large_Reconstruction_Models_with_Text_Instructions_WACV_2025_paper.html | 2 | 2501.04374 | Instructive3D: Editing Large Reconstruction Models with Text Instructions
Transformer based methods have enabled users to create modify and comprehend text and image data. Recently proposed Large Reconstruction Models (LRMs) further extend this by providing the ability to generate high-quality 3D models with the help o... | [
-0.030558211728930473,
0.0007223368738777936,
-0.022731661796569824,
-0.03157985955476761,
-0.017285913228988647,
-0.004843704868108034,
-0.001099753542803228,
-0.03004738874733448,
-0.02776692435145378,
-0.020159298554062843,
-0.03969831019639969,
0.0023716820869594812,
0.012788839638233185... | ||
wacv_2025_7aaaf6d91e | 7aaaf6d91e | wacv | 2,025 | Interactive Object Detection for Tiny Objects in Large Remotely Sensed Images | This paper highlights the potential of a Human-In-the-Loop (HIL) in interactive object detection methods. Although automation in computer vision is advancing rapidly certain critical tasks such as detecting UneXploded Ordnance (UXO) space/marine debris or the generation of new datasets require 100% recall and near-perf... | Marvin Burges; Sebastian Zambanini; Robert Sablatnig | Computer Vision Lab, TU Wien; Computer Vision Lab, TU Wien; Computer Vision Lab, TU Wien | Poster | main | https://github.com/mburges-cvl/WACV_IAODF | https://openaccess.thecvf.com/content/WACV2025/html/Burges_Interactive_Object_Detection_for_Tiny_Objects_in_Large_Remotely_Sensed_WACV_2025_paper.html | 1 | Interactive Object Detection for Tiny Objects in Large Remotely Sensed Images
This paper highlights the potential of a Human-In-the-Loop (HIL) in interactive object detection methods. Although automation in computer vision is advancing rapidly certain critical tasks such as detecting UneXploded Ordnance (UXO) space/mar... | [
-0.08878789842128754,
-0.03217628598213196,
-0.017682967707514763,
0.047714170068502426,
-0.0001206610759254545,
-0.0004980912199243903,
0.04055144637823105,
-0.017365867272019386,
0.00885081011801958,
0.006677745375782251,
-0.022178322076797485,
-0.04215559735894203,
-0.003980066627264023,
... | ||
wacv_2025_11d0977104 | 11d0977104 | wacv | 2,025 | Invariant Shape Representation Learning for Image Classification | Geometric shape features have been widely used as strong predictors for image classification. Nevertheless most existing classifiers such as deep neural networks (DNNs) directly leverage the statistical correlations between these shape features and target variables. However these correlations can often be spurious and ... | Tonmoy Hossain; Jing Ma; Jundong Li; Miaomiao Zhang | Computer Science, University of Virginia*; Electrical and Computer Engineering, Case Western Reserve University†; Computer Science, University of Virginia*; Computer Science, University of Virginia* | Poster | main | https://github.com/tonmoy-hossain/ISRL | https://openaccess.thecvf.com/content/WACV2025/html/Hossain_Invariant_Shape_Representation_Learning_for_Image_Classification_WACV_2025_paper.html | 2 | 2411.12201 | Invariant Shape Representation Learning for Image Classification
Geometric shape features have been widely used as strong predictors for image classification. Nevertheless most existing classifiers such as deep neural networks (DNNs) directly leverage the statistical correlations between these shape features and target... | [
-0.050147075206041336,
-0.012062651105225086,
0.0216817706823349,
0.006478090304881334,
-0.01014794409275055,
0.0028834566473960876,
-0.0026213242672383785,
-0.006920296233147383,
0.032221775501966476,
0.002995148068293929,
-0.015855593606829643,
-0.0062592667527496815,
-0.016712652519345284... | |
wacv_2025_6bb26ef138 | 6bb26ef138 | wacv | 2,025 | Inverse Problems with Diffusion Models: A MAP Estimation Perspective | Inverse problems have many applications in science and engineering. In Computer vision several image restoration tasks such as inpainting deblurring and super-resolution can be formally modeled as inverse problems. Recently methods have been developed for solving inverse problems that only leverage a pre-trained uncond... | Sai Bharath Chandra Gutha; Ricardo Vinuesa; Hossein Azizpour | RPL, KTH Royal Institute of Technology, Sweden; FLOW, KTH Royal Institute of Technology, Sweden; RPL, KTH Royal Institute of Technology, Sweden | Poster | main | https://openaccess.thecvf.com/content/WACV2025/html/Gutha_Inverse_Problems_with_Diffusion_Models_A_MAP_Estimation_Perspective_WACV_2025_paper.html | 1 | 2407.20784 | Inverse Problems with Diffusion Models: A MAP Estimation Perspective
Inverse problems have many applications in science and engineering. In Computer vision several image restoration tasks such as inpainting deblurring and super-resolution can be formally modeled as inverse problems. Recently methods have been developed... | [
-0.03419046849012375,
-0.015584663487970829,
-0.01290432270616293,
-0.002348752459511161,
-0.007990363985300064,
-0.03247725963592529,
0.017951836809515953,
0.020153217017650604,
0.04093276709318161,
0.042774926871061325,
-0.0450592041015625,
-0.007092311512678862,
-0.008869994431734085,
0... | ||
wacv_2025_e6aeef7e64 | e6aeef7e64 | wacv | 2,025 | Inverting the Generation Process of Denoising Diffusion Implicit Models: Empirical Evaluation and a Novel Method | This paper studies the problem of inverting the DDIM image generation process to recover latent variables particularly the initial noise map from a generated image. Existing methods often struggle with accuracy in this task. We propose a novel hybrid approach that combines direct inversion via gradient descent for the ... | Yan Zeng; Masanori Suganuma; Takayuki Okatani | Graduate School of Information Sciences, Tohoku University; RIKEN Center for AIP; Graduate School of Information Sciences, Tohoku University | Poster | main | https://openaccess.thecvf.com/content/WACV2025/html/Zeng_Inverting_the_Generation_Process_of_Denoising_Diffusion_Implicit_Models_Empirical_WACV_2025_paper.html | 1 | Inverting the Generation Process of Denoising Diffusion Implicit Models: Empirical Evaluation and a Novel Method
This paper studies the problem of inverting the DDIM image generation process to recover latent variables particularly the initial noise map from a generated image. Existing methods often struggle with accur... | [
-0.05253574252128601,
0.000501546950545162,
0.00864951591938734,
0.034209318459033966,
-0.01588289812207222,
-0.0010453233262524009,
0.00531743885949254,
0.03200644627213478,
0.016725173220038414,
0.03361695259809494,
-0.03076617419719696,
-0.0156977828592062,
-0.011680779978632927,
0.0109... | |||
wacv_2025_5a2c641673 | 5a2c641673 | wacv | 2,025 | Investigating Imaging Annotation and Self-Supervision for the Classification of Continuously Developing Cells in Histological Whole Slide Images | The analysis of individual cells is increasingly automated through deep learning techniques. This is particularly relevant for high-resolution whole slide images (WSIs) which can contain thousands of cells making manual evaluation impractical. This increase in automation however requires higher levels of standardisatio... | Sebastian Thiele; Jacqueline Kockwelp; Joachim Wistuba; Sabine Kliesch; Jörg Gromoll; Benjamin Risse | Institute for Geoinformatics, University of Münster+Faculty of Mathematics and Computer Science, University of Münster; Institute for Geoinformatics, University of Münster+Faculty of Mathematics and Computer Science, University of Münster+Centre of Reproductive Medicine and Andrology, University Hospital Münster; Centr... | Poster | main | https://openaccess.thecvf.com/content/WACV2025/html/Thiele_Investigating_Imaging_Annotation_and_Self-Supervision_for_the_Classification_of_Continuously_WACV_2025_paper.html | 0 | Investigating Imaging Annotation and Self-Supervision for the Classification of Continuously Developing Cells in Histological Whole Slide Images
The analysis of individual cells is increasingly automated through deep learning techniques. This is particularly relevant for high-resolution whole slide images (WSIs) which ... | [
-0.07760573923587799,
-0.041501566767692566,
-0.04937884211540222,
0.03756292909383774,
-0.008907520212233067,
-0.008574741892516613,
0.024707654491066933,
0.02786221168935299,
0.006245293188840151,
0.04044397175312042,
-0.0040822336450219154,
-0.031782615929841995,
-0.017532406374812126,
... | |||
wacv_2025_0a3ceeb1b8 | 0a3ceeb1b8 | wacv | 2,025 | InvisMark: Invisible and Robust Watermarking for AI-Generated Image Provenance | The proliferation of AI-generated images has intensified the need for robust content authentication methods. We present InvisMark a novel watermarking technique designed for high-resolution AI-generated images. Our approach leverages advanced neural network architectures and training strategies to embed imperceptible y... | Rui Xu; Mengya Hu; Deren Lei; Yaxi Li; David Lowe; Alex Gorevski; Mingyu Wang; Emily Ching; Alex Deng | Microsoft Responsible AI; Microsoft Responsible AI; Microsoft Responsible AI; Microsoft Responsible AI; Microsoft Responsible AI; Microsoft Responsible AI; Microsoft Responsible AI; Microsoft Responsible AI; Microsoft Responsible AI | Poster | main | https://openaccess.thecvf.com/content/WACV2025/html/Xu_InvisMark_Invisible_and_Robust_Watermarking_for_AI-Generated_Image_Provenance_WACV_2025_paper.html | 1 | 2411.07795 | InvisMark: Invisible and Robust Watermarking for AI-Generated Image Provenance
The proliferation of AI-generated images has intensified the need for robust content authentication methods. We present InvisMark a novel watermarking technique designed for high-resolution AI-generated images. Our approach leverages advance... | [
-0.015746530145406723,
0.000323866872349754,
0.005789302755147219,
0.009074113331735134,
-0.010915102437138557,
-0.015232548117637634,
-0.005023003090173006,
0.051211267709732056,
0.03192293643951416,
0.04638918489217758,
0.0025956076569855213,
-0.023400185629725456,
-0.009013369679450989,
... | ||
wacv_2025_43111b252a | 43111b252a | wacv | 2,025 | J-Invariant Volume Shuffle for Self-Supervised Cryo-Electron Tomogram Denoising on Single Noisy Volume | Cryo-Electron Tomography (Cryo-ET) enables detailed 3D visualization of cellular structures in near-native states but suffers from low signal-to-noise ratio due to imaging constraints. Traditional denoising methods and supervised learning approaches often struggle with complex noise patterns and the lack of paired data... | Xiwei Liu; Mohamad Kassab; Min Xu; Qirong Ho | Mohamed bin Zayed University of Artificial Intelligence; Mohamed bin Zayed University of Artificial Intelligence; Mohamed bin Zayed University of Artificial Intelligence + Carnegie Mellon University; Mohamed bin Zayed University of Artificial Intelligence | Poster | main | https://github.com/Xiwei-web/SelfCryoET | https://openaccess.thecvf.com/content/WACV2025/html/Liu_J-Invariant_Volume_Shuffle_for_Self-Supervised_Cryo-Electron_Tomogram_Denoising_on_Single_WACV_2025_paper.html | 0 | J-Invariant Volume Shuffle for Self-Supervised Cryo-Electron Tomogram Denoising on Single Noisy Volume
Cryo-Electron Tomography (Cryo-ET) enables detailed 3D visualization of cellular structures in near-native states but suffers from low signal-to-noise ratio due to imaging constraints. Traditional denoising methods an... | [
-0.03277105465531349,
-0.003001539269462228,
-0.0123030636459589,
-0.007923692464828491,
0.04275453835725784,
0.004852565936744213,
0.0015935528790578246,
0.021117474883794785,
-0.025366950780153275,
0.020282426849007607,
0.011059767566621304,
0.005228338297456503,
0.0016480630729347467,
-... | ||
wacv_2025_15c61243bd | 15c61243bd | wacv | 2,025 | Joint Co-Speech Gesture and Expressive Talking Face Generation using Diffusion with Adapters | Recent advances in co-speech gesture and talking head generation have been impressive yet most methods focus on only one of the two tasks. Those that attempt to generate both often rely on separate models or network modules increasing training complexity and ignoring the inherent relationship between face and body move... | Steven Hogue; Chenxu Zhang; Yapeng Tian; Xiaohu Guo | University of Texas at Dallas; University of Texas at Dallas; University of Texas at Dallas; University of Texas at Dallas | Poster | main | https://openaccess.thecvf.com/content/WACV2025/html/Hogue_Joint_Co-Speech_Gesture_and_Expressive_Talking_Face_Generation_using_Diffusion_WACV_2025_paper.html | 0 | 2412.14333 | Joint Co-Speech Gesture and Expressive Talking Face Generation using Diffusion with Adapters
Recent advances in co-speech gesture and talking head generation have been impressive yet most methods focus on only one of the two tasks. Those that attempt to generate both often rely on separate models or network modules inc... | [
-0.000778087240178138,
-0.015223095193505287,
-0.047770291566848755,
0.018070513382554054,
-0.027681704610586166,
-0.01959098130464554,
-0.030888507142663002,
0.027626415714621544,
0.009182696230709553,
-0.013536759652197361,
-0.02521209977567196,
-0.03339497372508049,
-0.022300174459815025,... | ||
wacv_2025_449f281a0f | 449f281a0f | wacv | 2,025 | Just Shift It: Test-Time Prototype Shifting for Zero-Shot Generalization with Vision-Language Models | Advancements in vision-language models (VLMs) have propelled the field of computer vision particularly in the zero-shot learning setting. Despite their promise the effectiveness of these models often diminishes due to domain shifts in test environments. To address this we introduce the Test-Time Prototype Shifting (TPS... | Elaine Sui; Xiaohan Wang; Serena Yeung-Levy | Stanford University; Stanford University; Stanford University | Poster | main | https://github.com/elaine-sui/TPS | https://openaccess.thecvf.com/content/WACV2025/html/Sui_Just_Shift_It_Test-Time_Prototype_Shifting_for_Zero-Shot_Generalization_with_WACV_2025_paper.html | 8 | 2403.12952 | Just Shift It: Test-Time Prototype Shifting for Zero-Shot Generalization with Vision-Language Models
Advancements in vision-language models (VLMs) have propelled the field of computer vision particularly in the zero-shot learning setting. Despite their promise the effectiveness of these models often diminishes due to d... | [
-0.06289338320493698,
-0.005838415119796991,
0.004286948125809431,
0.04699765518307686,
0.0032798557076603174,
0.011450007557868958,
-0.014634598046541214,
-0.013691015541553497,
0.029849862679839134,
0.034331876784563065,
-0.018799062818288803,
-0.034495189785957336,
-0.0012509269872680306,... | |
wacv_2025_c1a029f096 | c1a029f096 | wacv | 2,025 | KDC-MAE: Knowledge Distilled Contrastive Mask Auto-Encoder | In this work we attempted to extend the thought and showcase a way forward for the Self-supervised Learning (SSL) learning paradigm by combining contrastive learning self-distillation (knowledge distillation) and masked data modelling the three major SSL frameworks to learn a joint and coordinated representation. The p... | Maheswar Bora; Saurabh Atreya; Aritra Mukherjee; Abhijit Das | Machine Intelligence Group, Department of CS&IS, Birla Institute of Technology and Science, Pilani – Hyderabad Campus; Machine Intelligence Group, Department of CS&IS, Birla Institute of Technology and Science, Pilani – Hyderabad Campus; Machine Intelligence Group, Department of CS&IS, Birla Institute of Technology and... | Poster | main | https://openaccess.thecvf.com/content/WACV2025/html/Bora_KDC-MAE_Knowledge_Distilled_Contrastive_Mask_Auto-Encoder_WACV_2025_paper.html | 0 | KDC-MAE: Knowledge Distilled Contrastive Mask Auto-Encoder
In this work we attempted to extend the thought and showcase a way forward for the Self-supervised Learning (SSL) learning paradigm by combining contrastive learning self-distillation (knowledge distillation) and masked data modelling the three major SSL framew... | [
-0.03645087406039238,
0.0005904563004150987,
-0.0496668741106987,
-0.031525082886219025,
0.0031622666865587234,
-0.018550725653767586,
-0.017388982698321342,
0.017565567046403885,
-0.027491506189107895,
0.06676774471998215,
-0.008843193762004375,
-0.03570735827088356,
-0.04695304110646248,
... | |||
wacv_2025_dbb76adc99 | dbb76adc99 | wacv | 2,025 | Knockoff Branch: Model Stealing Attack via Adding Neurons in the Pre-Trained Model | We introduce Knockoff Branch: adding few neurons as a knockoff container for learning stolen features. Model stealing attacks extract the functionality from the victim model by querying APIs. Prior work substantially enhanced transferability and improved query efficiency between the adversary model and the victim model... | Li-Ying Hung; Cooper Cheng-Yuan Ku | National Yang Ming Chiao Tung University; National Yang Ming Chiao Tung University | Poster | main | https://github.com/onlyin-hung/knockoff-branch | https://openaccess.thecvf.com/content/WACV2025/html/Hung_Knockoff_Branch_Model_Stealing_Attack_via_Adding_Neurons_in_the_WACV_2025_paper.html | 0 | Knockoff Branch: Model Stealing Attack via Adding Neurons in the Pre-Trained Model
We introduce Knockoff Branch: adding few neurons as a knockoff container for learning stolen features. Model stealing attacks extract the functionality from the victim model by querying APIs. Prior work substantially enhanced transferabi... | [
-0.02201293222606182,
-0.03140268102288246,
-0.004103448241949081,
0.0056174080818891525,
-0.00006832658255007118,
-0.013226027600467205,
0.005197244230657816,
0.031676702201366425,
0.014331241138279438,
0.03291892632842064,
-0.024515647441148758,
0.006055840291082859,
-0.05096770450472832,
... | ||
wacv_2025_68d6155c38 | 68d6155c38 | wacv | 2,025 | LIME: Localized Image Editing via Attention Regularization in Diffusion Models | Diffusion models (DMs) have gained prominence due to their ability to generate high-quality varied images with recent advancements in text-to-image generation. The research focus is now shifting towards the controllability of DMs. A significant challenge within this domain is localized editing where specific areas of a... | Enis Simsar; Alessio Tonioni; Yongqin Xian; Thomas Hofmann; Federico Tombari | ETH Zürich - DALAB; Technical University of Munich; Google Switzerland; ETH Zürich - DALAB; Technical University of Munich + Google Switzerland | Poster | main | https://openaccess.thecvf.com/content/WACV2025/html/Simsar_LIME_Localized_Image_Editing_via_Attention_Regularization_in_Diffusion_Models_WACV_2025_paper.html | 9 | 2312.09256 | LIME: Localized Image Editing via Attention Regularization in Diffusion Models
Diffusion models (DMs) have gained prominence due to their ability to generate high-quality varied images with recent advancements in text-to-image generation. The research focus is now shifting towards the controllability of DMs. A signific... | [
-0.03929782286286354,
-0.02795075811445713,
-0.01729249209165573,
-0.008066204376518726,
-0.029763387516140938,
0.0060451216995716095,
-0.019667038694024086,
0.01886948011815548,
0.008283720351755619,
0.011156738735735416,
-0.04694712162017822,
-0.016259292140603065,
-0.026772547513246536,
... | ||
wacv_2025_5ad854313d | 5ad854313d | wacv | 2,025 | LIPIDS: Learning-Based Illumination Planning in Discretized (Light) Space for Photometric Stereo | Photometric stereo is a powerful technique for estimating per-pixel surface normals from images under varied illumination. Although several methods address photometric stereo with different image (or light) counts ranging from one to two to a hundred very few focus on learning optimal lighting configuration. Finding an... | Ashish Tiwari; Mihirkumar Sutariya; Shanmuganathan Raman | CVIG Lab, IIT Gandhinagar; CVIG Lab, IIT Gandhinagar; CVIG Lab, IIT Gandhinagar | Poster | main | https://openaccess.thecvf.com/content/WACV2025/html/Tiwari_LIPIDS_Learning-Based_Illumination_Planning_in_Discretized_Light_Space_for_Photometric_WACV_2025_paper.html | 1 | 2409.02716 | LIPIDS: Learning-Based Illumination Planning in Discretized (Light) Space for Photometric Stereo
Photometric stereo is a powerful technique for estimating per-pixel surface normals from images under varied illumination. Although several methods address photometric stereo with different image (or light) counts ranging f... | [
-0.07103023678064346,
0.01108926348388195,
-0.05132007971405983,
0.015749702230095863,
-0.030302057042717934,
-0.020023304969072342,
0.020520664751529694,
-0.008768254891037941,
0.031259935349226,
0.025365309789776802,
-0.031259935349226,
-0.008432077243924141,
-0.021828534081578255,
0.039... | ||
wacv_2025_4f6d8027c6 | 4f6d8027c6 | wacv | 2,025 | LLM-Generated Rewrite and Context Modulation for Enhanced Vision Language Models in Digital Pathology | Recent advancements in vision-language models (VLMs) have found important applications in medical imaging particularly in digital pathology. VLMs demand large-scale datasets of image-caption pairs which is often hard to obtain in medical domains. State-of-the-art VLMs in digital pathology have been pre-trained on datas... | Cagla Deniz Bahadir; Gozde B. Akar; Mert R. Sabuncu | Cornell University and Cornell Tech, New York City, NY, USA; Middle East Technical University, Ankara, Turkey; Cornell University and Cornell Tech, Weill Cornell Medicine, New York City, NY, USA | Poster | main | https://openaccess.thecvf.com/content/WACV2025/html/Bahadir_LLM-Generated_Rewrite_and_Context_Modulation_for_Enhanced_Vision_Language_Models_WACV_2025_paper.html | 0 | LLM-Generated Rewrite and Context Modulation for Enhanced Vision Language Models in Digital Pathology
Recent advancements in vision-language models (VLMs) have found important applications in medical imaging particularly in digital pathology. VLMs demand large-scale datasets of image-caption pairs which is often hard t... | [
-0.055268771946430206,
0.00022612018801737577,
-0.009462959133088589,
0.01294452790170908,
-0.013544484972953796,
0.041451580822467804,
-0.007890344597399235,
0.026707179844379425,
0.01248092483729124,
-0.004786021076142788,
-0.03332488611340523,
-0.014371698722243309,
-0.012535466812551022,... | |||
wacv_2025_2d5c9b95bd | 2d5c9b95bd | wacv | 2,025 | LLM-RSPF: Large Language Model-Based Robotic System Planning Framework for Domain Specific Use-Cases | The employment of large language models (LLMs) for task planning and reasoning has emerged as a focal point of interest within the robotics research community. However directly applying LLMs even with large token-sized prompts does not achieve the task planning performance required for an industrial-grade domain-specif... | Chandan Kumar Singh; Devesh Kumar; Vipul Sanap; Rajesh Sinha | Tata Consultancy Services (TCS) Research, New Delhi, India; Tata Consultancy Services (TCS) Research, New Delhi, India; Tata Consultancy Services (TCS) Research, New Delhi, India; Tata Consultancy Services (TCS) Research, New Delhi, India | Poster | main | https://openaccess.thecvf.com/content/WACV2025/html/Singh_LLM-RSPF_Large_Language_Model-Based_Robotic_System_Planning_Framework_for_Domain_WACV_2025_paper.html | 0 | LLM-RSPF: Large Language Model-Based Robotic System Planning Framework for Domain Specific Use-Cases
The employment of large language models (LLMs) for task planning and reasoning has emerged as a focal point of interest within the robotics research community. However directly applying LLMs even with large token-sized ... | [
-0.03298201039433479,
-0.0069536627270281315,
0.0012272479943931103,
-0.005532638635486364,
-0.039984673261642456,
0.009336884133517742,
-0.004488029982894659,
-0.025943709537386894,
0.014611154794692993,
0.007122938055545092,
-0.017096834257245064,
0.013524227775633335,
0.004318754654377699... | |||
wacv_2025_b4b5d6f987 | b4b5d6f987 | wacv | 2,025 | LLS: Local Learning Rule for Deep Neural Networks Inspired by Neural Activity Synchronization | Training deep neural networks (DNNs) using traditional backpropagation (BP) presents challenges in terms of computational complexity and energy consumption particularly for on-device learning where computational resources are limited. Various alternatives to BP including random feedback alignment forward-forward and lo... | Marco P. E. Apolinario; Arani Roy; Kaushik Roy | ;; | Poster | main | https://github.com/mapolinario94/LLS-DNN | https://openaccess.thecvf.com/content/WACV2025/html/Apolinario_LLS_Local_Learning_Rule_for_Deep_Neural_Networks_Inspired_by_WACV_2025_paper.html | 3 | 2405.15868 | LLS: Local Learning Rule for Deep Neural Networks Inspired by Neural Activity Synchronization
Training deep neural networks (DNNs) using traditional backpropagation (BP) presents challenges in terms of computational complexity and energy consumption particularly for on-device learning where computational resources are ... | [
-0.03194870427250862,
-0.022855324670672417,
-0.04972298443317413,
0.006763788405805826,
-0.026230188086628914,
-0.02028667740523815,
0.037179745733737946,
-0.01994919218122959,
0.010302707552909851,
0.017239926382899284,
-0.0386609323322773,
-0.00039461292908526957,
-0.008498093113303185,
... | |
wacv_2025_730ccbb13b | 730ccbb13b | wacv | 2,025 | LLaVA-SpaceSGG: Visual Instruct Tuning for Open-Vocabulary Scene Graph Generation with Enhanced Spatial Relations | Scene Graph Generation (SGG) converts visual scenes into structured graph representations providing deeper scene understanding for complex vision tasks. However existing SGG models often overlook essential spatial relationships and struggle with generalization in open-vocabulary contexts. To address these limitations w... | Mingjie Xu; Mengyang Wu; Yuzhi Zhao; Jason Chun Lok Li; Weifeng Ou | Independent Researcher; The Chinese University of Hong Kong; City University of Hong Kong; The University of Hong Kong; Dongguan University of Technology | Poster | main | https://github.com/Endlinc/LLaVA-SpaceSGG | https://openaccess.thecvf.com/content/WACV2025/html/Xu_LLaVA-SpaceSGG_Visual_Instruct_Tuning_for_Open-Vocabulary_Scene_Graph_Generation_with_WACV_2025_paper.html | 1 | LLaVA-SpaceSGG: Visual Instruct Tuning for Open-Vocabulary Scene Graph Generation with Enhanced Spatial Relations
Scene Graph Generation (SGG) converts visual scenes into structured graph representations providing deeper scene understanding for complex vision tasks. However existing SGG models often overlook essential ... | [
-0.036348745226860046,
-0.0022131113801151514,
-0.008572561666369438,
-0.019104307517409325,
-0.0122245904058218,
-0.020765552297234535,
-0.01127659808844328,
-0.000282422814052552,
0.025478430092334747,
-0.02815086580812931,
-0.008847931399941444,
0.023347703740000725,
0.0028078637551516294... | ||
wacv_2025_ccc7489f2b | ccc7489f2b | wacv | 2,025 | LORD: Large Models Based Opposite Reward Design for Autonomous Driving | Reinforcement learning (RL) based autonomous driving has emerged as a promising alternative to data-driven imitation learning approaches. However crafting effective reward functions for RL poses challenges due to the complexity of defining and quantifying good driving behaviors across diverse scenarios. Recently large ... | Xin Ye; Feng Tao; Abhirup Mallik; Burhaneddin Yaman; Liu Ren | Bosch Research North America & Bosch Center for Artificial Intelligence (BCAI); Bosch Research North America & Bosch Center for Artificial Intelligence (BCAI); Bosch Research North America & Bosch Center for Artificial Intelligence (BCAI); Bosch Research North America & Bosch Center for Artificial Intelligence (BCAI); ... | Poster | main | https://openaccess.thecvf.com/content/WACV2025/html/Ye_LORD_Large_Models_Based_Opposite_Reward_Design_for_Autonomous_Driving_WACV_2025_paper.html | 5 | 2403.18965 | LORD: Large Models Based Opposite Reward Design for Autonomous Driving
Reinforcement learning (RL) based autonomous driving has emerged as a promising alternative to data-driven imitation learning approaches. However crafting effective reward functions for RL poses challenges due to the complexity of defining and quant... | [
-0.056723758578300476,
0.0005108866607770324,
0.0007334769470617175,
-0.031214740127325058,
-0.01638542301952839,
-0.011152091436088085,
0.0033576686400920153,
0.033400699496269226,
0.004872092977166176,
-0.014403240755200386,
-0.03328954800963402,
-0.018441705033183098,
-0.0285286046564579,... | ||
wacv_2025_e81b5708f2 | e81b5708f2 | wacv | 2,025 | LQ-Adapter: ViT-Adapter with Learnable Queries for Gallbladder Cancer Detection from Ultrasound Images | We focus on the problem of Gallbladder Cancer (GBC) detection from Ultrasound (US) images. The problem presents unique challenges to modern Deep Neural Network (DNN) techniques due to low image quality arising from noise textures and viewpoint variations. Tackling such challenges would necessitate precise localization ... | Chetan Madan; Mayuna Gupta; Soumen Basu; Pankaj Gupta; Chetan Arora | IIT Delhi; IIT Delhi + University of California San Diego; IIT Delhi + Samsung R&D Institute Bangalore; PGIMER, Chandigarh; IIT Delhi | Poster | main | https://github.com/ChetanMadan/LQ-Adapter | https://openaccess.thecvf.com/content/WACV2025/html/Madan_LQ-Adapter_ViT-Adapter_with_Learnable_Queries_for_Gallbladder_Cancer_Detection_from_WACV_2025_paper.html | 0 | LQ-Adapter: ViT-Adapter with Learnable Queries for Gallbladder Cancer Detection from Ultrasound Images
We focus on the problem of Gallbladder Cancer (GBC) detection from Ultrasound (US) images. The problem presents unique challenges to modern Deep Neural Network (DNN) techniques due to low image quality arising from no... | [
-0.0509364940226078,
-0.010350486263632774,
-0.035186152905225754,
0.008108951151371002,
-0.01908743381500244,
-0.006697103846818209,
0.027118457481265068,
-0.03142734244465828,
0.0035525362472981215,
0.039128322154283524,
-0.010983067564666271,
-0.03938502073287964,
-0.028145255520939827,
... | ||
wacv_2025_be850695d5 | be850695d5 | wacv | 2,025 | Label Augmented Dataset Distillation | Traditional dataset distillation primarily focuses on image representation while often overlooking the important role of labels. In this study we introduce Label-Augmented Dataset Distillation (LADD) a new dataset distillation framework enhancing dataset distillation with label augmentations. LADD sub-samples each synt... | Seoungyoon Kang; Youngsun Lim; Hyunjung Shim | Yonsei University+KAIST AI; KAIST AI; KAIST AI | Poster | main | https://openaccess.thecvf.com/content/WACV2025/html/Kang_Label_Augmented_Dataset_Distillation_WACV_2025_paper.html | 3 | 2409.16239 | Label Augmented Dataset Distillation
Traditional dataset distillation primarily focuses on image representation while often overlooking the important role of labels. In this study we introduce Label-Augmented Dataset Distillation (LADD) a new dataset distillation framework enhancing dataset distillation with label augm... | [
-0.06765440106391907,
-0.032931115478277206,
-0.061941858381032944,
0.0032343058846890926,
-0.035488687455654144,
-0.01702561043202877,
-0.0063285985961556435,
0.04715646058320999,
-0.025239719077944756,
0.017809683457016945,
-0.021879402920603752,
-0.018080376088619232,
-0.05738675966858864... | ||
wacv_2025_492bfd2a3c | 492bfd2a3c | wacv | 2,025 | Label Calibration in Source Free Domain Adaptation | Source-free domain adaptation (SFDA) utilizes a pre-trained source model with unlabeled target data. Self-supervised SFDA techniques generate pseudolabels from the pre-trained source model but these pseudolabels often contain noise due to domain discrepancies between the source and target domains. Traditional self-supe... | Shivangi Rai; Rini Smita Thakur; Kunal Jangid; Vinod K Kurmi | Indian Institute of Science Education and Research Bhopal, India; Indian Institute of Science Education and Research Bhopal, India; Indian Institute of Science Education and Research Bhopal, India; Indian Institute of Science Education and Research Bhopal, India | Poster | main | https://openaccess.thecvf.com/content/WACV2025/html/Rai_Label_Calibration_in_Source_Free_Domain_Adaptation_WACV_2025_paper.html | 0 | 2501.07072 | Label Calibration in Source Free Domain Adaptation
Source-free domain adaptation (SFDA) utilizes a pre-trained source model with unlabeled target data. Self-supervised SFDA techniques generate pseudolabels from the pre-trained source model but these pseudolabels often contain noise due to domain discrepancies between t... | [
-0.047879017889499664,
-0.019524360075592995,
-0.04777044802904129,
0.01665632426738739,
0.0002055463701253757,
-0.0074912747368216515,
0.033964063972234726,
0.004786092322319746,
0.03325836360454559,
0.025169959291815758,
-0.014493987895548344,
-0.00599392456933856,
-0.029965098947286606,
... | ||
wacv_2025_13abf63f98 | 13abf63f98 | wacv | 2,025 | Label Convergence: Defining an Upper Performance Bound in Object Recognition through Contradictory Annotations | Annotation errors are a challenge not only during training of machine learning models but also during their evaluation. Label variations and inaccuracies in datasets often manifest as contradictory examples that deviate from established labeling conventions. Such inconsistencies when significant prevent models from ach... | David Eike Tschirschwitz; Volker Rodehorst | Bauhaus-Universität Weimar, Germany; Bauhaus-Universität Weimar, Germany | Poster | main | https://openaccess.thecvf.com/content/WACV2025/html/Tschirschwitz_Label_Convergence_Defining_an_Upper_Performance_Bound_in_Object_Recognition_WACV_2025_paper.html | 1 | 2409.09412 | Label Convergence: Defining an Upper Performance Bound in Object Recognition through Contradictory Annotations
Annotation errors are a challenge not only during training of machine learning models but also during their evaluation. Label variations and inaccuracies in datasets often manifest as contradictory examples th... | [
-0.1023292988538742,
-0.039143156260252,
-0.03595452755689621,
0.04167206585407257,
-0.006500952411442995,
-0.014394638128578663,
-0.03485500067472458,
-0.00014359706256072968,
0.004448502324521542,
0.0165387149900198,
-0.027873005717992783,
-0.0064413947984576225,
-0.010940291918814182,
0... | ||
wacv_2025_34d7ec6ece | 34d7ec6ece | wacv | 2,025 | Language-Guided Instance-Aware Domain-Adaptive Panoptic Segmentation | The increasing relevance of panoptic segmentation is tied to the advancements in autonomous driving and AR/VR applications. However the deployment of such models has been limited due to the expensive nature of dense data annotation giving rise to unsupervised domain adaptation (UDA). A key challenge in panoptic UDA is ... | Elham Amin Mansour; Ozan Unal; Suman Saha; Benjamin Bejar; Luc Van Gool | ETH Zurich; ETH Zurich + Huawei Technologies; ETH Zurich + PSI; PSI; ETH Zurich + KU Leuven + INSAIT | Poster | main | https://github.com/elhamAm/LIDAPS | https://openaccess.thecvf.com/content/WACV2025/html/Mansour_Language-Guided_Instance-Aware_Domain-Adaptive_Panoptic_Segmentation_WACV_2025_paper.html | 1 | 2404.03799 | Language-Guided Instance-Aware Domain-Adaptive Panoptic Segmentation
The increasing relevance of panoptic segmentation is tied to the advancements in autonomous driving and AR/VR applications. However the deployment of such models has been limited due to the expensive nature of dense data annotation giving rise to unsu... | [
-0.041423484683036804,
-0.016785217449069023,
-0.006996107753366232,
0.01243248675018549,
-0.04577621817588806,
-0.02325083687901497,
0.051507312804460526,
0.002618439495563507,
0.016640126705169678,
0.02724083885550499,
-0.037088893353939056,
-0.02154601737856865,
-0.006098357029259205,
0... | |
wacv_2025_ec5f659dfb | ec5f659dfb | wacv | 2,025 | Latency Robust Cooperative Perception using Asynchronous Feature Fusion | Recent advancements in cooperative perception have showcased substantial improvements compared to single-agent perception. Nonetheless the inherent latency present in such systems can dramatically impair their effectiveness. In this paper we propose a Latency Robust Cooperative Perception framework named LRCP to compen... | Junjie Wang; Tomas Nordström | Department of Applied Physics and Electronics, Umeå University; Department of Applied Physics and Electronics, Umeå University+RISE Research Institutes of Sweden | Poster | main | https://github.com/JesseWong333/LRCP | https://openaccess.thecvf.com/content/WACV2025/html/Wang_Latency_Robust_Cooperative_Perception_using_Asynchronous_Feature_Fusion_WACV_2025_paper.html | 0 | Latency Robust Cooperative Perception using Asynchronous Feature Fusion
Recent advancements in cooperative perception have showcased substantial improvements compared to single-agent perception. Nonetheless the inherent latency present in such systems can dramatically impair their effectiveness. In this paper we propos... | [
-0.0643603652715683,
0.014632419683039188,
0.009256676770746708,
0.0022828339133411646,
0.0001051763174473308,
0.010110853239893913,
-0.0179748497903347,
0.02126157470047474,
0.017854152247309685,
0.02167009375989437,
-0.020723070949316025,
-0.039143580943346024,
-0.00835607759654522,
0.03... | ||
wacv_2025_97c525ec41 | 97c525ec41 | wacv | 2,025 | LatteCLIP: Unsupervised CLIP Fine-Tuning via LMM-Synthetic Texts | Large-scale vision-language pre-trained (VLP) models (e.g. CLIP) are renowned for their versatility as they can be applied to diverse applications in a zero-shot setup. However when these models are used in specific domains their performance often falls short due to domain gaps or the under-representation of these doma... | Anh-Quan Cao; Maximilian Jaritz; Matthieu Guillaumin; Raoul de Charette; Loris Bazzani | Inria; Amazon; Amazon; Inria; Amazon | Poster | main | https://openaccess.thecvf.com/content/WACV2025/html/Cao_LatteCLIP_Unsupervised_CLIP_Fine-Tuning_via_LMM-Synthetic_Texts_WACV_2025_paper.html | 2 | 2410.08211 | LatteCLIP: Unsupervised CLIP Fine-Tuning via LMM-Synthetic Texts
Large-scale vision-language pre-trained (VLP) models (e.g. CLIP) are renowned for their versatility as they can be applied to diverse applications in a zero-shot setup. However when these models are used in specific domains their performance often falls s... | [
-0.07622948288917542,
-0.02941383793950081,
-0.01203962229192257,
0.004787336569279432,
0.011359002441167831,
0.014734511263668537,
0.012775428593158722,
0.01581982523202896,
-0.05669383704662323,
0.02056577242910862,
-0.027482347562909126,
-0.033129654824733734,
-0.01603136956691742,
0.03... | ||
wacv_2025_3ac9a57e30 | 3ac9a57e30 | wacv | 2,025 | Learning Anatomy-Disease Entangled Representation | Human experts demonstrate proficiency not only in disentangling anatomical structures from disease conditions but also in intertwining anatomical and disease information to accurately diagnose a variety of disorders. However deep learning models despite their prowess in acquiring intricate representation often struggle... | Fatemeh Haghighi; Michael B. Gotway; Jianming Liang | Arizona State University, USA; Mayo Clinic, USA; Arizona State University, USA | Poster | main | GitHub.com/JLiangLab/LeADER | https://openaccess.thecvf.com/content/WACV2025/html/Haghighi_Learning_Anatomy-Disease_Entangled_Representation_WACV_2025_paper.html | 0 | Learning Anatomy-Disease Entangled Representation
Human experts demonstrate proficiency not only in disentangling anatomical structures from disease conditions but also in intertwining anatomical and disease information to accurately diagnose a variety of disorders. However deep learning models despite their prowess in... | [
-0.016017314046621323,
-0.057691991329193115,
-0.04782947897911072,
-0.009992282837629318,
-0.005960146430879831,
0.01746332086622715,
0.006446783430874348,
0.010400131344795227,
0.0008597256382927299,
-0.0010422145714983344,
0.011401212774217129,
-0.004502552095800638,
-0.010743094608187675... | ||
wacv_2025_72bea8d262 | 72bea8d262 | wacv | 2,025 | Learning Deep Illumination-Robust Features from Multispectral Filter Array Images | Multispectral (MS) snapshot cameras equipped with a MS filter array (MSFA) capture multiple spectral bands in a single shot resulting in a raw mosaic image where each pixel holds only one channel value. The fully-defined MS image is estimated from the raw one through demosaicing which inevitably introduces spatio-spect... | Anis Amziane | Luxembourg Institute of Science and Technology (LIST), ENVISION Unit | Poster | main | https://github.com/AnisAmziane/RawTexture | https://openaccess.thecvf.com/content/WACV2025/html/Amziane_Learning_Deep_Illumination-Robust_Features_from_Multispectral_Filter_Array_Images_WACV_2025_paper.html | 0 | 2407.15472 | Learning Deep Illumination-Robust Features from Multispectral Filter Array Images
Multispectral (MS) snapshot cameras equipped with a MS filter array (MSFA) capture multiple spectral bands in a single shot resulting in a raw mosaic image where each pixel holds only one channel value. The fully-defined MS image is estim... | [
-0.044466156512498856,
-0.0051177856512367725,
-0.03449396416544914,
0.014585915952920914,
-0.008210253901779652,
-0.03540217876434326,
-0.0022228537127375603,
0.04086962714791298,
0.02134302817285061,
0.04126924276351929,
-0.033785559237003326,
-0.01956292800605297,
-0.009890450164675713,
... | |
wacv_2025_d6ebea4c92 | d6ebea4c92 | wacv | 2,025 | Learning Instance-Specific Parameters of Black-Box Models using Differentiable Surrogates | Tuning parameters of a non-differentiable or black-box compute is challenging. Existing methods rely mostly on random sampling or grid sampling from the parameter space. Further with all the current methods it is not possible to supply any input specific parameters to the black-box. To the best of our knowledge for the... | Arnisha Khondaker; Nilanjan Ray | University of Alberta, Canada; University of Alberta, Canada | Poster | main | https://github.com/arnisha-k/instance-specific-param | https://openaccess.thecvf.com/content/WACV2025/html/Khondaker_Learning_Instance-Specific_Parameters_of_Black-Box_Models_using_Differentiable_Surrogates_WACV_2025_paper.html | 1 | 2407.17530 | Learning Instance-Specific Parameters of Black-Box Models using Differentiable Surrogates
Tuning parameters of a non-differentiable or black-box compute is challenging. Existing methods rely mostly on random sampling or grid sampling from the parameter space. Further with all the current methods it is not possible to s... | [
-0.042168278247117996,
0.008762059733271599,
-0.03778954595327377,
-0.010689252987504005,
-0.02119452692568302,
-0.0010423632338643074,
0.031147858127951622,
-0.0009176015737466514,
0.029786404222249985,
0.04588467627763748,
-0.050263408571481705,
0.0026493158657103777,
-0.016273055225610733... | |
wacv_2025_aaa6f35611 | aaa6f35611 | wacv | 2,025 | Learning Keypoints for Multi-Agent Behavior Analysis using Self-Supervision | The study of social interactions and collective behaviors through multi-agent video analysis is crucial in biology. While self-supervised keypoint discovery has emerged as a promising solution to reduce the need for manual keypoint annotations existing methods often struggle with videos containing multiple interacting ... | Daniel Khalil; Christina Liu; Pietro Perona; Jennifer Sun; Markus Marks | California Institute of Technology; California Institute of Technology; California Institute of Technology; California Institute of Technology+Cornell University; California Institute of Technology | Poster | main | https://openaccess.thecvf.com/content/WACV2025/html/Khalil_Learning_Keypoints_for_Multi-Agent_Behavior_Analysis_using_Self-Supervision_WACV_2025_paper.html | 1 | 2409.09455 | Learning Keypoints for Multi-Agent Behavior Analysis using Self-Supervision
The study of social interactions and collective behaviors through multi-agent video analysis is crucial in biology. While self-supervised keypoint discovery has emerged as a promising solution to reduce the need for manual keypoint annotations ... | [
-0.047870222479104996,
-0.047685395926237106,
-0.059847019612789154,
0.022271666675806046,
-0.018658297136425972,
0.023121871054172516,
0.04032927751541138,
0.0275022741407156,
-0.03650335595011711,
0.024341730400919914,
-0.022955527529120445,
-0.03870279714465141,
-0.0034424050245434046,
... | ||
wacv_2025_b022b69049 | b022b69049 | wacv | 2,025 | Learning Multiple Object States from Actions via Large Language Models | Recognizing the states of objects in a video is crucial in understanding the scene beyond actions and objects. For instance an egg can be raw cracked and whisked while cooking an omelet and these states can coexist simultaneously (an egg can be both raw and whisked). However most existing research assumes a single obje... | Masatoshi Tateno; Takuma Yagi; Ryosuke Furuta; Yoichi Sato | The University of Tokyo; National Institute of Advanced Industrial Science and Technology (AIST); The University of Tokyo; The University of Tokyo | Poster | main | https://masatate.github.io/ObjStatefromAction.github.io/ | https://openaccess.thecvf.com/content/WACV2025/html/Tateno_Learning_Multiple_Object_States_from_Actions_via_Large_Language_Models_WACV_2025_paper.html | 1 | 2405.01090 | Learning Multiple Object States from Actions via Large Language Models
Recognizing the states of objects in a video is crucial in understanding the scene beyond actions and objects. For instance an egg can be raw cracked and whisked while cooking an omelet and these states can coexist simultaneously (an egg can be both... | [
-0.021855853497982025,
-0.03654085099697113,
-0.005416284780949354,
0.015114104375243187,
-0.002731981687247753,
0.028301995247602463,
-0.0047201779671013355,
0.030781280249357224,
-0.029770495370030403,
-0.019738925620913506,
-0.018241818994283676,
-0.04054585099220276,
-0.00625542737543582... | |
wacv_2025_13fe4cc552 | 13fe4cc552 | wacv | 2,025 | Learning Semantic Part-Based Graph Structure for 3D Point Cloud Domain Generalization | In 3D data analysis point clouds provide detailed geometric insights for applications like computer vision and geospatial analysis. However their irregularity and diversity make classification challenging especially in domain generalization where models must generalize to new data distributions. Our research introduces... | G Ujwal Sai; Arkadipta De; Vartika Sengar; Anuj Rathore; Daksh Thapar; Manohar Kaul | Fujitsu Research India, Bangalore; Fujitsu Research India, Bangalore; Fujitsu Research India, Bangalore; Fujitsu Research India, Bangalore; Fujitsu Research India, Bangalore; Fujitsu Research India, Bangalore | Poster | main | https://openaccess.thecvf.com/content/WACV2025/html/Sai_Learning_Semantic_Part-Based_Graph_Structure_for_3D_Point_Cloud_Domain_WACV_2025_paper.html | 0 | Learning Semantic Part-Based Graph Structure for 3D Point Cloud Domain Generalization
In 3D data analysis point clouds provide detailed geometric insights for applications like computer vision and geospatial analysis. However their irregularity and diversity make classification challenging especially in domain generali... | [
-0.03993762657046318,
-0.011997761204838753,
-0.011521877720952034,
-0.025734327733516693,
0.0072892578318715096,
-0.02172592282295227,
0.021799135953187943,
-0.015502828173339367,
0.0054452079348266125,
0.013809779658913612,
-0.033311862498521805,
-0.0008001939277164638,
0.02661288343369960... | |||
wacv_2025_e3bd8bd0da | e3bd8bd0da | wacv | 2,025 | Learning Semi-Supervised Medical Image Segmentation from Spatial Registration | Semi-supervised medical image segmentation has shown promise in training models with limited labeled data and abundant unlabeled data. However state-of-the-art methods ignore a potentially valuable source of unsupervised semantic information--spatial registration transforms between image volumes. To address this we pro... | Qianying Liu; Paul Henderson; Xiao Gu; Hang Dai; Fani Deligianni | University of Glasgow; University of Glasgow; University of Oxford; University of Glasgow; University of Glasgow | Poster | main | https://github.com/kathyliu579/ContrastiveCross-teachingWithRegistration | https://openaccess.thecvf.com/content/WACV2025/html/Liu_Learning_Semi-Supervised_Medical_Image_Segmentation_from_Spatial_Registration_WACV_2025_paper.html | 0 | 2409.10422 | Learning Semi-Supervised Medical Image Segmentation from Spatial Registration
Semi-supervised medical image segmentation has shown promise in training models with limited labeled data and abundant unlabeled data. However state-of-the-art methods ignore a potentially valuable source of unsupervised semantic information-... | [
-0.08335047215223312,
-0.028236348181962967,
-0.016982199624180794,
0.025500837713479996,
0.010712554678320885,
-0.007201370783150196,
0.00012127069203415886,
0.011171533726155758,
0.006113591603934765,
0.03550656512379646,
-0.006099822465330362,
-0.0015283979009836912,
-0.006687314715236425... | |
wacv_2025_fdc298fdd7 | fdc298fdd7 | wacv | 2,025 | Learning Unified Distance Metric Across Diverse Data Distributions with Parameter-Efficient Transfer Learning | A common practice in metric learning is to train and test an embedding model for each dataset. This dataset-specific approach fails to simulate real-world scenarios that involve multiple heterogeneous distributions of data. In this regard we explore a new metric learning paradigm called Unified Metric Learning (UML) wh... | Sungyeon Kim; Donghyun Kim; Suha Kwak | Pohang University of Science and Technology (POSTECH); Korea University; Pohang University of Science and Technology (POSTECH) | Poster | main | https://openaccess.thecvf.com/content/WACV2025/html/Kim_Learning_Unified_Distance_Metric_Across_Diverse_Data_Distributions_with_Parameter-Efficient_WACV_2025_paper.html | 0 | 2309.08944 | Learning Unified Distance Metric Across Diverse Data Distributions with Parameter-Efficient Transfer Learning
A common practice in metric learning is to train and test an embedding model for each dataset. This dataset-specific approach fails to simulate real-world scenarios that involve multiple heterogeneous distribut... | [
-0.06289367377758026,
-0.011940411292016506,
-0.04336847364902496,
-0.01784490793943405,
-0.03689136356115341,
-0.022585401311516762,
-0.022134819999337196,
0.019694168120622635,
-0.01026011724025011,
0.0039543225429952145,
-0.04329337924718857,
0.011161280795931816,
0.013864770531654358,
... | ||
wacv_2025_da87d251bf | da87d251bf | wacv | 2,025 | Learning Visual Grounding from Generative Vision and Language Model | Visual grounding tasks aim to localize image regions based on natural language references. In this work we explore whether generative VLMs predominantly trained on image-text data could be leveraged to scale up the text annotation of visual grounding data. We find that grounding knowledge already exists in generative V... | Shijie Wang; Dahun Kim; Ali Taalimi; Chen Sun; Weicheng Kuo | Brown University; Google DeepMind; Google Cloud; Brown University; Google DeepMind | Poster | main | https://openaccess.thecvf.com/content/WACV2025/html/Wang_Learning_Visual_Grounding_from_Generative_Vision_and_Language_Model_WACV_2025_paper.html | 7 | 2407.14563 | Learning Visual Grounding from Generative Vision and Language Model
Visual grounding tasks aim to localize image regions based on natural language references. In this work we explore whether generative VLMs predominantly trained on image-text data could be leveraged to scale up the text annotation of visual grounding d... | [
-0.05515967309474945,
-0.016681058332324028,
-0.002343921223655343,
0.028692515566945076,
-0.02477077953517437,
-0.019353311508893967,
0.012421870604157448,
-0.0022823591716587543,
-0.011874652467668056,
-0.015550139360129833,
-0.017994385212659836,
-0.02380402572453022,
0.007177689112722874... | ||
wacv_2025_607c2b6921 | 607c2b6921 | wacv | 2,025 | Learning Visual-Semantic Hierarchical Attribute Space for Interpretable Open-Set Recognition | In the field of open-set recognition conventional models often focus on addressing challenges within a single hierarchical category and these methods frequently lack interpretability. In this paper we propose a novel solution that utilizes attributes and hierarchical relationships to achieve interpretable open-set reco... | Zhuo Xu; Xiang Xiang | National Key Lab of Multi-Spectral Information Intelligent Processing Technology, School of Artificial Intelligence and Automation, Huazhong University of Science and Technology, Wuhan, China; National Key Lab of Multi-Spectral Information Intelligent Processing Technology, School of Artificial Intelligence and Automat... | Poster | main | https://openaccess.thecvf.com/content/WACV2025/html/Xu_Learning_Visual-Semantic_Hierarchical_Attribute_Space_for_Interpretable_Open-Set_Recognition_WACV_2025_paper.html | 0 | Learning Visual-Semantic Hierarchical Attribute Space for Interpretable Open-Set Recognition
In the field of open-set recognition conventional models often focus on addressing challenges within a single hierarchical category and these methods frequently lack interpretability. In this paper we propose a novel solution t... | [
-0.04810555651783943,
-0.024071553722023964,
-0.004053390584886074,
0.04851863905787468,
0.010402216576039791,
-0.030474362894892693,
-0.004670670256018639,
0.02052278444170952,
0.0826168805360794,
0.010045461356639862,
-0.03984386846423149,
-0.013199924491345882,
0.03289654105901718,
0.02... | |||
wacv_2025_e60b462a7f | e60b462a7f | wacv | 2,025 | Learning the Power of "No": Foundation Models with Negations | Negation is a fundamental aspect of natural language reasoning yet foundational vision-language models (VLMs) like CLIP face significant challenges in accurately interpreting it. These models often process text prompts holistically making it difficult to isolate and understand the role of negated terms. To overcome thi... | Jaisidh Singh; Ishaan Shrivastava; Mayank Vatsa; Richa Singh; Aparna Bharati | University of Tübingen, Germany; Metafusion, India; IIT Jodhpur, India; IIT Jodhpur, India; Lehigh University, USA | Poster | main | https://github.com/jaisidhsingh/CoN-CLIP | https://openaccess.thecvf.com/content/WACV2025/html/Singh_Learning_the_Power_of_No_Foundation_Models_with_Negations_WACV_2025_paper.html | 0 | Learning the Power of "No": Foundation Models with Negations
Negation is a fundamental aspect of natural language reasoning yet foundational vision-language models (VLMs) like CLIP face significant challenges in accurately interpreting it. These models often process text prompts holistically making it difficult to isol... | [
-0.05623217299580574,
-0.028244303539395332,
-0.0006015881081111729,
-0.004368525464087725,
0.008947692811489105,
-0.04747680574655533,
-0.019745368510484695,
0.0248557198792696,
-0.025844819843769073,
0.02282256819307804,
-0.00035889222635887563,
-0.0630093365907669,
-0.0513233058154583,
... | ||
wacv_2025_9a863e4be1 | 9a863e4be1 | wacv | 2,025 | Learning to Count from Pseudo-Labeled Segmentation | Class-agnostic counting (CAC) has numerous potential applications across various domains. The goal is to count objects of an arbitrary category during testing based on only a few annotated exemplars. However existing methods often count all objects in the image including those from different categories than the exempla... | Jingyi Xu; Hieu Le; Dimitris Samaras | Stony Brook University; EPFL; Stony Brook University | Poster | main | https://openaccess.thecvf.com/content/WACV2025/html/Xu_Learning_to_Count_from_Pseudo-Labeled_Segmentation_WACV_2025_paper.html | 0 | Learning to Count from Pseudo-Labeled Segmentation
Class-agnostic counting (CAC) has numerous potential applications across various domains. The goal is to count objects of an arbitrary category during testing based on only a few annotated exemplars. However existing methods often count all objects in the image includi... | [
-0.01744295470416546,
-0.02665897272527218,
-0.041855696588754654,
0.037862397730350494,
-0.007187015376985073,
-0.03989602252840996,
0.028359822928905487,
-0.02514299936592579,
-0.006392052862793207,
0.05886419489979744,
-0.020336247980594635,
-0.008444164879620075,
-0.00278699048794806,
... | |||
wacv_2025_00f13c1a36 | 00f13c1a36 | wacv | 2,025 | Learning to Identify Seen Unseen and Unknown in the Open World: A Practical Setting for Zero-Shot Learning | As vision-language models advance addressing the Zero-Shot Learning (ZSL) problem in the open world becomes increasingly crucial. Specifically a robust model must handle three types of samples during inference: seen classes with visual and semantic information provided in training unseen classes with only the semantic ... | Sethupathy Parameswaran; Yuan Fang; Chandan Gautam; Savitha Ramasamy; Xiaoli Li | Singapore Management University; Singapore Management University; Institute for Infocomm Research, A*STAR; Institute for Infocomm Research, A*STAR; A*STAR Centre for Frontier AI Research | Poster | main | https://github.com/smufang/OZSL | https://openaccess.thecvf.com/content/WACV2025/html/Parameswaran_Learning_to_Identify_Seen_Unseen_and_Unknown_in_the_Open_WACV_2025_paper.html | 0 | Learning to Identify Seen Unseen and Unknown in the Open World: A Practical Setting for Zero-Shot Learning
As vision-language models advance addressing the Zero-Shot Learning (ZSL) problem in the open world becomes increasingly crucial. Specifically a robust model must handle three types of samples during inference: se... | [
-0.04593557119369507,
0.0007764304173178971,
-0.030423395335674286,
0.016038011759519577,
0.011108295992016792,
0.04555997624993324,
0.009535481221973896,
0.03474276885390282,
0.009371156804263592,
0.014122465625405312,
-0.009596515446901321,
0.017061514779925346,
0.01616947166621685,
-0.0... | ||
wacv_2025_af19828cf3 | af19828cf3 | wacv | 2,025 | Learning to Visually Connect Actions and their Effects | We introduce the novel concept of visually Connecting Actions and Their Effects (CATE) in video understanding. CATE can have applications in areas like task planning and learning from demonstration. We identify and explore two different aspects of the concept of CATE: Action Selection (AS) and Effect-Affinity Assessmen... | Paritosh Parmar; Eric Peh; Basura Fernando | ;; | Poster | main | https://openaccess.thecvf.com/content/WACV2025/html/Parmar_Learning_to_Visually_Connect_Actions_and_their_Effects_WACV_2025_paper.html | 2 | 2401.10805 | Learning to Visually Connect Actions and their Effects
We introduce the novel concept of visually Connecting Actions and Their Effects (CATE) in video understanding. CATE can have applications in areas like task planning and learning from demonstration. We identify and explore two different aspects of the concept of CA... | [
-0.0030062347650527954,
-0.027543673291802406,
-0.026841217651963234,
0.05749048665165901,
0.010204099118709564,
-0.01594390533864498,
0.01012091338634491,
0.05116838216781616,
-0.020426684990525246,
0.048913128674030304,
0.021609768271446228,
-0.03418003395199776,
-0.003389812773093581,
0... | ||
wacv_2025_f7a1e5c9e7 | f7a1e5c9e7 | wacv | 2,025 | Learning under Noisy Labels Spurious Points and Diverse Structures: TS40K a 3D Point Cloud Dataset of Rural Terrain and Electrical Transmission Systems | Research in 3D scene understanding particularly in autonomous driving and indoor segmentation has made significant strides. However most available datasets focus on urban settings. We introduce TS40K a 3D point cloud dataset spanning 40000 km of electrical transmission systems in rural terrain addressing power-grid ins... | Diogo Lavado; Ricardo Santos; André Coelho; João Santos; Alessandra Micheletti; Cláudia Soares | NOV A SST & UniMi; EDP - Labelec; EDP - Labelec; CNET - Centre New Energy; UniMi; NOV A SST | Poster | main | https://github.com/dlavado/TS40K | https://openaccess.thecvf.com/content/WACV2025/html/Lavado_Learning_under_Noisy_Labels_Spurious_Points_and_Diverse_Structures_TS40K_WACV_2025_paper.html | 0 | Learning under Noisy Labels Spurious Points and Diverse Structures: TS40K a 3D Point Cloud Dataset of Rural Terrain and Electrical Transmission Systems
Research in 3D scene understanding particularly in autonomous driving and indoor segmentation has made significant strides. However most available datasets focus on urb... | [
-0.06212857738137245,
0.008305189199745655,
-0.03740356117486954,
-0.02755073457956314,
-0.0017207589698955417,
-0.02093260921537876,
0.008300541900098324,
-0.010968240909278393,
0.006343918852508068,
0.01223237719386816,
0.001318745082244277,
0.01628504879772663,
-0.011507358402013779,
0.... | ||
wacv_2025_e0cef82314 | e0cef82314 | wacv | 2,025 | Leveraging CLIP Encoder for Multimodal Emotion Recognition | Multimodal emotion recognition (MER) aims to identify human emotions by combining data from various modalities such as language audio and vision. Despite the recent advances of MER approaches the limitations in obtaining extensive datasets impede the improvement of performance. To mitigate this issue we leverage a Cont... | Yehun Song; Sunyoung Cho | Agency for Defense Development, Daejeon, Republic of Korea; Sookmyung Women’s University, Seoul, Republic of Korea | Poster | main | https://openaccess.thecvf.com/content/WACV2025/html/Song_Leveraging_CLIP_Encoder_for_Multimodal_Emotion_Recognition_WACV_2025_paper.html | 0 | Leveraging CLIP Encoder for Multimodal Emotion Recognition
Multimodal emotion recognition (MER) aims to identify human emotions by combining data from various modalities such as language audio and vision. Despite the recent advances of MER approaches the limitations in obtaining extensive datasets impede the improvemen... | [
-0.0645737498998642,
-0.058272965252399445,
-0.027402833104133606,
0.011977088637650013,
-0.029266972094774246,
-0.03219366818666458,
-0.0224442258477211,
0.01560283824801445,
0.014661448076367378,
-0.005634358152747154,
-0.017308523878455162,
-0.011790675111114979,
-0.00741927046328783,
0... | |||
wacv_2025_527d9ea376 | 527d9ea376 | wacv | 2,025 | Leveraging Vision Language Models for Specialized Agricultural Tasks | As Vision Language Models (VLMs) become increasingly accessible to farmers and agricultural experts there is a growing need to evaluate their potential in specialized tasks. We present AgEval a comprehensive benchmark for assessing VLMs' capabilities in plant stress phenotyping offering a solution to the challenge of l... | Muhammad Arbab Arshad; Talukder Zaki Jubery; Tirtho Roy; Rim Nassiri; Asheesh K. Singh; Arti Singh; Chinmay Hegde; Baskar Ganapathysubramanian; Aditya Balu; Adarsh Krishnamurthy; Soumik Sarkar | Iowa State University, USA; Iowa State University, USA; Iowa State University, USA; Iowa State University, USA; Iowa State University, USA; Iowa State University, USA; New York University, USA; Iowa State University, USA; Iowa State University, USA; Iowa State University, USA; Iowa State University, USA | Poster | main | https://github.com/arbab-ml/AgEval | https://openaccess.thecvf.com/content/WACV2025/html/Arshad_Leveraging_Vision_Language_Models_for_Specialized_Agricultural_Tasks_WACV_2025_paper.html | 1 | Leveraging Vision Language Models for Specialized Agricultural Tasks
As Vision Language Models (VLMs) become increasingly accessible to farmers and agricultural experts there is a growing need to evaluate their potential in specialized tasks. We present AgEval a comprehensive benchmark for assessing VLMs' capabilities ... | [
-0.053705718368291855,
-0.027653893455863,
0.0010980088263750076,
0.010076649487018585,
0.01235231477767229,
-0.01643941178917885,
0.01718582957983017,
0.03104008361697197,
-0.0069953971542418,
-0.005893975030630827,
-0.03420781344175339,
0.007141040172427893,
-0.030712388455867767,
0.0547... | ||
wacv_2025_aae5b893e5 | aae5b893e5 | wacv | 2,025 | LiCamPose: Combining Multi-View LiDAR and RGB Cameras for Robust Single-Timestamp 3D Human Pose Estimation | Several methods have been proposed to estimate 3D human pose from multi-view images achieving satisfactory performance on public datasets collected under relatively simple conditions. However there are limited approaches studying extracting 3D human skeletons from multimodal inputs such as RGB and point cloud data. To ... | Zhiyu Pan; Zhicheng Zhong; Wenxuan Guo; Yifan Chen; Jianjiang Feng; Jie Zhou | Department of Automation, BNRist, Tsinghua University, China; Department of Automation, BNRist, Tsinghua University, China; Department of Automation, BNRist, Tsinghua University, China; Department of Automation, BNRist, Tsinghua University, China; Department of Automation, BNRist, Tsinghua University, China; Department... | Poster | main | https://github.com/Yu-Yy/LiCamPose | https://openaccess.thecvf.com/content/WACV2025/html/Pan_LiCamPose_Combining_Multi-View_LiDAR_and_RGB_Cameras_for_Robust_Single-Timestamp_WACV_2025_paper.html | 0 | LiCamPose: Combining Multi-View LiDAR and RGB Cameras for Robust Single-Timestamp 3D Human Pose Estimation
Several methods have been proposed to estimate 3D human pose from multi-view images achieving satisfactory performance on public datasets collected under relatively simple conditions. However there are limited app... | [
-0.019597670063376427,
-0.011628073640167713,
-0.045225393027067184,
-0.010184905491769314,
-0.016729718074202538,
-0.004182429518550634,
0.02753969095647335,
0.020958108827471733,
-0.015157859772443771,
0.02673078142106533,
-0.02064557559788227,
-0.00869577657431364,
0.020811034366488457,
... | ||
wacv_2025_5e96a9ad98 | 5e96a9ad98 | wacv | 2,025 | LiGAR: LiDAR-Guided Hierarchical Transformer for Multi-Modal Group Activity Recognition | Group Activity Recognition (GAR) remains challenging in computer vision due to the complex nature of multi-agent interactions. This paper introduces LiGAR a LIDAR-Guided Hierarchical Transformer for Multi-Modal Group Activity Recognition. LiGAR leverages LiDAR data as a structural backbone to guide the processing of vi... | Naga Venkata Sai Raviteja Chappa; Khoa Luu | CVIU Lab, Department of EECS, University of Arkansas, Fayetteville USA; CVIU Lab, Department of EECS, University of Arkansas, Fayetteville USA | Poster | main | https://uark-cviu.github.io/projects/LiGAR/ | https://openaccess.thecvf.com/content/WACV2025/html/Chappa_LiGAR_LiDAR-Guided_Hierarchical_Transformer_for_Multi-Modal_Group_Activity_Recognition_WACV_2025_paper.html | 1 | 2410.21108 | LiGAR: LiDAR-Guided Hierarchical Transformer for Multi-Modal Group Activity Recognition
Group Activity Recognition (GAR) remains challenging in computer vision due to the complex nature of multi-agent interactions. This paper introduces LiGAR a LIDAR-Guided Hierarchical Transformer for Multi-Modal Group Activity Recogn... | [
-0.03519239276647568,
-0.042442720383405685,
-0.04978436231613159,
0.03163114935159683,
-0.005099883768707514,
-0.010446314699947834,
0.02472781576216221,
0.01630866900086403,
0.0038420341443270445,
0.015158114023506641,
-0.03563069924712181,
-0.017303992062807083,
-0.023723362013697624,
0... | |
wacv_2025_9b414b1d83 | 9b414b1d83 | wacv | 2,025 | LiLMaps: Learnable Implicit Language Maps | One of the current trends in robotics is to employ large language models (LLMs) to provide non-predefined commands execution and natural human-robot interaction. It is useful to have an environment map together with its language representation which can be further utilized by LLMs. Such a comprehensive scene representa... | Evgenii Kruzhkov; Sven Behnke | University of Bonn; University of Bonn | Poster | main | https://openaccess.thecvf.com/content/WACV2025/html/Kruzhkov_LiLMaps_Learnable_Implicit_Language_Maps_WACV_2025_paper.html | 0 | 2501.03304 | LiLMaps: Learnable Implicit Language Maps
One of the current trends in robotics is to employ large language models (LLMs) to provide non-predefined commands execution and natural human-robot interaction. It is useful to have an environment map together with its language representation which can be further utilized by L... | [
-0.05841076746582985,
0.0023615146055817604,
0.003937411587685347,
0.015003660693764687,
-0.031033052131533623,
0.0001644959265831858,
0.00013739561836700886,
-0.016290487721562386,
0.02165226824581623,
0.011674693785607815,
-0.026650380343198776,
-0.03332696110010147,
0.019824599847197533,
... | ||
wacv_2025_13a87c198d | 13a87c198d | wacv | 2,025 | Lifting by Gaussians: A Simple Fast and Flexible Method for 3D Instance Segmentation | We introduce Lifting By Gaussians (LBG) a novel approach for open-world instance segmentation of 3D Gaussian Splatted Radiance Fields (3DGS). Recently 3DGS Fields have emerged as a highly efficient and explicit alternative to Neural Field-based methods for high-quality Novel View Synthesis. Our 3D instance segmentation... | Rohan Chacko; Nicolai Häeni; Eldar Khaliullin; Lin Sun; Douglas Lee | Magic Leap Inc.; Magic Leap Inc.; Magic Leap Inc.; Magic Leap Inc.; Magic Leap Inc. | Poster | main | https://openaccess.thecvf.com/content/WACV2025/html/Chacko_Lifting_by_Gaussians_A_Simple_Fast_and_Flexible_Method_for_WACV_2025_paper.html | 1 | Lifting by Gaussians: A Simple Fast and Flexible Method for 3D Instance Segmentation
We introduce Lifting By Gaussians (LBG) a novel approach for open-world instance segmentation of 3D Gaussian Splatted Radiance Fields (3DGS). Recently 3DGS Fields have emerged as a highly efficient and explicit alternative to Neural Fi... | [
-0.038169752806425095,
-0.02014000527560711,
-0.005442244000732899,
-0.015123515389859676,
0.00866316445171833,
-0.0035980823449790478,
0.008417893201112747,
-0.015410436317324638,
-0.0180667694658041,
0.019029343500733376,
-0.040687255561351776,
0.03265346586704254,
0.009588715620338917,
... | |||
wacv_2025_28709bfe17 | 28709bfe17 | wacv | 2,025 | LoSA: Long-Short-Range Adapter for Scaling End-to-End Temporal Action Localization | Temporal Action Localization (TAL) involves localizing and classifying action snippets in an untrimmed video. The emergence of large video foundation models has led RGB-only video backbones to outperform previous methods needing both RGB and optical flow modalities. Leveraging these large models is often limited to tra... | Akshita Gupta; Gaurav Mittal; Ahmed Magooda; Ye Yu; Graham Taylor; Mei Chen | Microsoft+University of Guelph+Vector Institute for AI; Microsoft+University of Guelph+Vector Institute for AI; Microsoft; Microsoft; University of Guelph+Vector Institute for AI; Microsoft | Poster | main | https://openaccess.thecvf.com/content/WACV2025/html/Gupta_LoSA_Long-Short-Range_Adapter_for_Scaling_End-to-End_Temporal_Action_Localization_WACV_2025_paper.html | 3 | 2404.01282 | LoSA: Long-Short-Range Adapter for Scaling End-to-End Temporal Action Localization
Temporal Action Localization (TAL) involves localizing and classifying action snippets in an untrimmed video. The emergence of large video foundation models has led RGB-only video backbones to outperform previous methods needing both RGB... | [
-0.021351808682084084,
-0.00795405451208353,
-0.032551854848861694,
0.049692150205373764,
-0.039577167481184006,
0.008542562834918499,
0.027457579970359802,
0.0578577034175396,
-0.009719578549265862,
0.040386367589235306,
0.006809222511947155,
-0.011678207665681839,
0.028082869946956635,
0... | ||
wacv_2025_f09441a441 | f09441a441 | wacv | 2,025 | Local Masked Reconstruction for Efficient Self-Supervised Learning on High-Resolution Images | Self-supervised learning for computer vision has progressed tremendously and improved many downstream vision tasks such as image classification semantic segmentation and object detection. Among these generative self-supervised vision learning approaches such as MAE and BEiT show promising performance. However their glo... | Jun Chen; Faizan Farooq Khan; Ming Hu; Ammar Sherif; Zongyuan Ge; Boyang Li; Mohamed Elhoseiny | King Abdullah University of Science and Technology; King Abdullah University of Science and Technology; Monash University; Nile University; Monash University; Nanyang Technological University; King Abdullah University of Science and Technology | Poster | main | https://github.com/junchen14/LoMaR | https://openaccess.thecvf.com/content/WACV2025/html/Chen_Local_Masked_Reconstruction_for_Efficient_Self-Supervised_Learning_on_High-Resolution_Images_WACV_2025_paper.html | 0 | Local Masked Reconstruction for Efficient Self-Supervised Learning on High-Resolution Images
Self-supervised learning for computer vision has progressed tremendously and improved many downstream vision tasks such as image classification semantic segmentation and object detection. Among these generative self-supervised ... | [
-0.05057493597269058,
0.016248224303126335,
-0.02713640406727791,
0.0020053484477102757,
0.001990174176171422,
0.027790067717432976,
0.00965555477887392,
0.04228273779153824,
-0.015771983191370964,
0.0418345108628273,
-0.03832339867949486,
-0.01794775202870369,
-0.04725058749318123,
0.0510... | ||
wacv_2025_7710a19158 | 7710a19158 | wacv | 2,025 | Localized Gaussian Splatting Editing with Contextual Awareness | Recent advancements in text-guided 3D object generation using diffusion priors struggle with illumination inconsistencies when applied to scene editing tasks like object replacement or insertion. To address this we propose an illumination-aware 3D scene editing pipeline for 3D Gaussian Splatting (3DGS). Our method leve... | Hanyuan Xiao; Yingshu Chen; Huajian Huang; Haolin Xiong; Jing Yang; Pratusha Prasad; Yajie Zhao | University of Southern California + Institute for Creative Technologies; HKUST; HKUST; University of California, Los Angeles; University of Southern California + Institute for Creative Technologies; University of Southern California + Institute for Creative Technologies; University of Southern California + Institute fo... | Poster | main | https://openaccess.thecvf.com/content/WACV2025/html/Xiao_Localized_Gaussian_Splatting_Editing_with_Contextual_Awareness_WACV_2025_paper.html | 3 | 2408.00083 | Localized Gaussian Splatting Editing with Contextual Awareness
Recent advancements in text-guided 3D object generation using diffusion priors struggle with illumination inconsistencies when applied to scene editing tasks like object replacement or insertion. To address this we propose an illumination-aware 3D scene edi... | [
-0.08043206483125687,
-0.011210908181965351,
-0.008892517536878586,
-0.015682416036725044,
-0.006716445088386536,
-0.055824995040893555,
-0.03992221876978874,
-0.009218470193445683,
-0.0005385666154325008,
0.04337455332279205,
-0.00846097618341446,
0.013212528079748154,
0.0329991839826107,
... | ||
wacv_2025_00d944c573 | 00d944c573 | wacv | 2,025 | LogicNet: A Logical Consistency Embedded Face Attribute Learning Network | Ensuring logical consistency in predictions is a crucial yet overlooked aspect in face attribute classification. We explore the potential reasons for this oversight and introduce two pressing challenges to the field: 1) How can we ensure that a model when trained with data checked for logical consistency yields predict... | Haiyu Wu; Sicong Tian; Huayu Li; Kevin W. Bowyer | ;;; | Poster | main | https://openaccess.thecvf.com/content/WACV2025/html/Wu_LogicNet_A_Logical_Consistency_Embedded_Face_Attribute_Learning_Network_WACV_2025_paper.html | 2 | 2311.11208 | LogicNet: A Logical Consistency Embedded Face Attribute Learning Network
Ensuring logical consistency in predictions is a crucial yet overlooked aspect in face attribute classification. We explore the potential reasons for this oversight and introduce two pressing challenges to the field: 1) How can we ensure that a mo... | [
-0.0471758209168911,
-0.02136121317744255,
-0.0256070364266634,
-0.02373887412250042,
-0.021983932703733444,
-0.021115897223353386,
0.011576946824789047,
-0.006623485591262579,
0.010869309306144714,
0.012822388671338558,
-0.03302307426929474,
-0.027645030990242958,
-0.00001583891389600467,
... | ||
wacv_2025_818f2bfafd | 818f2bfafd | wacv | 2,025 | Long-Term Ad Memorability: Understanding & Generating Memorable Ads | Despite the importance of long-term memory in marketing and brand building until now there has been no large-scale study on the memorability of ads. All previous memorability studies have been conducted on short-term recall on specific content types like action videos. On the other hand long-term memorability is crucia... | Harini Si; Somesh Singh; Yaman Kumar Singla; Aanisha Bhattacharyya; Veeky Baths; Changyou Chen; Rajiv Ratn Shah; Balaji Krishnamurthy | ;;;;;;; | Poster | main | https://behavior-in-the-wild.github.io/memorability | https://openaccess.thecvf.com/content/WACV2025/html/Si_Long-Term_Ad_Memorability_Understanding__Generating_Memorable_Ads_WACV_2025_paper.html | 1 | 2309.00378 | Long-Term Ad Memorability: Understanding & Generating Memorable Ads
Despite the importance of long-term memory in marketing and brand building until now there has been no large-scale study on the memorability of ads. All previous memorability studies have been conducted on short-term recall on specific content types li... | [
-0.051925722509622574,
-0.008895736187696457,
-0.010557472705841064,
-0.014733118936419487,
-0.042798008769750595,
-0.04935026913881302,
-0.013559014536440372,
0.03088652715086937,
-0.01449640467762947,
0.005638542585074902,
-0.025811364874243736,
-0.00012124227941967547,
-0.0134075172245502... | |
wacv_2025_edc25e993d | edc25e993d | wacv | 2,025 | Looking at Model Debiasing through the Lens of Anomaly Detection | Deep neural networks are likely to learn unintended spurious correlations between training data and labels when dealing with biased data potentially limiting the generalization to unseen samples not presenting the same bias. In this context model debiasing approaches can be devised aiming at reducing the model's depend... | Vito Paolo Pastore; Massimiliano Ciranni; Davide Marinelli; Francesca Odone; Vittorio Murino | MaLGa, DIBRIS, University of Genoa, Italy+Istituto Italiano di Tecnologia, Genoa, Italy+University of Verona, Italy; MaLGa, DIBRIS, University of Genoa, Italy+Istituto Italiano di Tecnologia, Genoa, Italy+University of Verona, Italy; MaLGa, DIBRIS, University of Genoa, Italy; MaLGa, DIBRIS, University of Genoa, Italy; ... | Poster | main | https://github.com/Malga-Vision/MoDAD | https://openaccess.thecvf.com/content/WACV2025/html/Pastore_Looking_at_Model_Debiasing_through_the_Lens_of_Anomaly_Detection_WACV_2025_paper.html | 0 | 2407.17449 | Looking at Model Debiasing through the Lens of Anomaly Detection
Deep neural networks are likely to learn unintended spurious correlations between training data and labels when dealing with biased data potentially limiting the generalization to unseen samples not presenting the same bias. In this context model debiasin... | [
-0.043382689356803894,
-0.04889160022139549,
-0.037801288068294525,
0.02903052046895027,
-0.03167624771595001,
-0.013056485913693905,
0.001643386553041637,
0.017242534086108208,
0.02884930558502674,
0.03680461272597313,
-0.061250410974025726,
-0.016997896134853363,
-0.0031780446879565716,
... | |
wacv_2025_37f5ea809f | 37f5ea809f | wacv | 2,025 | Loose Social-Interaction Recognition in Real-World Therapy Scenarios | The computer vision community has explored dyadic interactions for atomic actions such as pushing carrying-object etc. However with the advancement in deep learning models there is a need to explore more complex dyadic situations such as loose interactions. These are interactions where two people perform certain atomic... | Abid Ali; Rui Dai; Ashish Marisetty; Guillaume Astruc; Monique Thonnat; Jean-Marc Odobez; Susanne Thummler; Francois Bremond | ;;;;;;; | Poster | main | https://openaccess.thecvf.com/content/WACV2025/html/Ali_Loose_Social-Interaction_Recognition_in_Real-World_Therapy_Scenarios_WACV_2025_paper.html | 1 | 2409.20270 | Loose Social-Interaction Recognition in Real-World Therapy Scenarios
The computer vision community has explored dyadic interactions for atomic actions such as pushing carrying-object etc. However with the advancement in deep learning models there is a need to explore more complex dyadic situations such as loose interac... | [
0.008009105920791626,
-0.014989858493208885,
-0.04710039496421814,
0.008078589104115963,
0.007036339957267046,
0.01112658903002739,
0.021437907591462135,
0.035834841430187225,
0.010802333243191242,
0.03518633171916008,
0.013516813516616821,
-0.0036223947536200285,
-0.0064619448967278,
-0.0... | ||
wacv_2025_4bd5e0d339 | 4bd5e0d339 | wacv | 2,025 | Low-Frequency Black-Box Backdoor Attack via Evolutionary Algorithm | Convolutional Neural Networks (CNNs) that have excelled in diverse computer vision tasks are vulnerable to backdoor attacks enabling attacker-controlled predictions via specific triggers. Restricted to spatial domains recent research exploits perceptual traits by embedding triggers in the frequency domain yielding pixe... | Yanqi Qiao; Dazhuang Liu; Rui Wang; Kaitai Liang | Delft University of Technology; Delft University of Technology; Delft University of Technology; Delft University of Technology | Poster | main | https://openaccess.thecvf.com/content/WACV2025/html/Qiao_Low-Frequency_Black-Box_Backdoor_Attack_via_Evolutionary_Algorithm_WACV_2025_paper.html | 1 | 2402.15653 | Low-Frequency Black-Box Backdoor Attack via Evolutionary Algorithm
Convolutional Neural Networks (CNNs) that have excelled in diverse computer vision tasks are vulnerable to backdoor attacks enabling attacker-controlled predictions via specific triggers. Restricted to spatial domains recent research exploits perceptual... | [
-0.05934026464819908,
0.0045313965529203415,
-0.030927184969186783,
0.042296115309000015,
-0.002701275981962681,
0.033090054988861084,
0.02726694382727146,
0.011775622144341469,
0.026268696412444115,
0.055199384689331055,
-0.02974407561123371,
0.02758120745420456,
-0.032553959637880325,
0.... | ||
wacv_2025_8829dd687a | 8829dd687a | wacv | 2,025 | LowFormer: Hardware Efficient Design for Convolutional Transformer Backbones | Research in efficient vision backbones is evolving into models that are a mixture of convolutions and transformer blocks. A smart combination of both architecture-wise and component-wise is mandatory to excel in the speed-accuracy trade-off. Most publications focus on maximizing accuracy and utilize MACs (multiply accu... | Moritz Nottebaum; Matteo Dunnhofer; Christian Micheloni | University of Udine, Italy; University of Udine, Italy; University of Udine, Italy | Poster | main | https://github.com/altair199797/LowFormer | https://openaccess.thecvf.com/content/WACV2025/html/Nottebaum_LowFormer_Hardware_Efficient_Design_for_Convolutional_Transformer_Backbones_WACV_2025_paper.html | 2 | 2409.03460 | LowFormer: Hardware Efficient Design for Convolutional Transformer Backbones
Research in efficient vision backbones is evolving into models that are a mixture of convolutions and transformer blocks. A smart combination of both architecture-wise and component-wise is mandatory to excel in the speed-accuracy trade-off. M... | [
-0.0037358691915869713,
-0.005111762788146734,
-0.07169224321842194,
0.027335628867149353,
-0.009986616671085358,
0.028593067079782486,
0.002170904539525509,
0.012373927980661392,
0.03360459953546524,
0.016702068969607353,
-0.03320367634296417,
-0.009840826503932476,
0.011836327612400055,
... | |
wacv_2025_e36c72eb80 | e36c72eb80 | wacv | 2,025 | LumiGauss: Relightable Gaussian Splatting in the Wild | Decoupling lighting from geometry using unconstrained photo collections is notoriously challenging. Solving it would benefit many users as creating complex 3D assets takes days of manual labor. Many previous works have attempted to address this issue often at the expense of output fidelity which questions the practical... | Joanna Kaleta; Kacper Kania; Tomasz Trzcinski; Marek Kowalski | Warsaw University of Technology+IDEAS NCBR+Tooploox; Warsaw University of Technology; Warsaw University of Technology+IDEAS NCBR+Tooploox; Microsoft | Poster | main | https://github.com/joaxkal/lumigauss | https://openaccess.thecvf.com/content/WACV2025/html/Kaleta_LumiGauss_Relightable_Gaussian_Splatting_in_the_Wild_WACV_2025_paper.html | 0 | 2408.04474 | LumiGauss: Relightable Gaussian Splatting in the Wild
Decoupling lighting from geometry using unconstrained photo collections is notoriously challenging. Solving it would benefit many users as creating complex 3D assets takes days of manual labor. Many previous works have attempted to address this issue often at the ex... | [
-0.08255821466445923,
0.014592955820262432,
-0.026827646419405937,
-0.016130546107888222,
-0.038826506584882736,
-0.03871330991387367,
0.006206958554685116,
0.0064286356791853905,
0.012555413879454136,
0.04237334057688713,
-0.02933684177696705,
0.028997251763939857,
0.010404674336314201,
0... | |
wacv_2025_5a96876b64 | 5a96876b64 | wacv | 2,025 | MAGMA: Manifold Regularization for MAEs | Masked Autoencoders (MAEs) are an important divide in self-supervised learning (SSL) due to their independence from augmentation techniques for generating positive (and/or negative) pairs as in contrastive frameworks. Their masking and reconstruction strategy also nicely aligns with SSL approaches in natural language p... | Alin-Eugen Dondera; Anuj R Singh; Hadi Jamali-Rad | Delft University of Technology (TU Delft), The Netherlands + Shell Global Solutions International B.V., Amsterdam, The Netherlands; Delft University of Technology (TU Delft), The Netherlands + Shell Global Solutions International B.V., Amsterdam, The Netherlands; Delft University of Technology (TU Delft), The Netherlan... | Poster | main | https://github.com/adondera/magma | https://openaccess.thecvf.com/content/WACV2025/html/Dondera_MAGMA_Manifold_Regularization_for_MAEs_WACV_2025_paper.html | 0 | 2412.02871 | MAGMA: Manifold Regularization for MAEs
Masked Autoencoders (MAEs) are an important divide in self-supervised learning (SSL) due to their independence from augmentation techniques for generating positive (and/or negative) pairs as in contrastive frameworks. Their masking and reconstruction strategy also nicely aligns w... | [
-0.019936202093958855,
-0.00715368939563632,
-0.02927364967763424,
-0.020651571452617645,
-0.003819223027676344,
0.007520786486566067,
-0.0017460649833083153,
0.049699313938617706,
-0.03070438653230667,
0.04073837772011757,
-0.027014588937163353,
-0.034507136791944504,
-0.016161689534783363,... | |
wacv_2025_f129900e98 | f129900e98 | wacv | 2,025 | MAISI: Medical AI for Synthetic Imaging | Medical imaging analysis faces challenges such as data scarcity high annotation costs and privacy concerns. This paper introduces the Medical AI for Synthetic Imaging (MAISI) an innovative approach using the diffusion model to generate synthetic 3D computed tomography (CT) images to address those challenges. MAISI leve... | Pengfei Guo; Can Zhao; Dong Yang; Ziyue Xu; Vishwesh Nath; Yucheng Tang; Benjamin Simon; Mason Belue; Stephanie Harmon; Baris Turkbey; Daguang Xu | NVIDIA; NVIDIA; NVIDIA; NVIDIA; NVIDIA; NVIDIA; National Institutes of Health; University of Arkansas for Medical Sciences; National Institutes of Health; National Institutes of Health; NVIDIA | Poster | main | https://openaccess.thecvf.com/content/WACV2025/html/Guo_MAISI_Medical_AI_for_Synthetic_Imaging_WACV_2025_paper.html | 15 | 2409.11169 | MAISI: Medical AI for Synthetic Imaging
Medical imaging analysis faces challenges such as data scarcity high annotation costs and privacy concerns. This paper introduces the Medical AI for Synthetic Imaging (MAISI) an innovative approach using the diffusion model to generate synthetic 3D computed tomography (CT) images... | [
-0.008358210325241089,
-0.03347013518214226,
-0.034682147204875946,
0.027540607377886772,
0.008717152290046215,
-0.014823820441961288,
-0.01566290482878685,
0.051538415253162384,
0.020921165123581886,
0.050867147743701935,
-0.03524153679609299,
-0.01267017051577568,
0.012856634333729744,
0... | ||
wacv_2025_4f3446316a | 4f3446316a | wacv | 2,025 | MDCN-PS: Monocular-Depth-Guided Coarse Normal Attention for Robust Photometric Stereo | Photometric Stereo (PS) is a technique for estimating surface normals from images illuminated by multiple light sources. However when the target object has a complex shape or the light sources are not appropriately arranged certain regions may experience severe shadows leading to insufficient information for accurate e... | Masahiro Yamaguchi; Takashi Shibata; Shoji Yachida; Keiko Yokoyama; Toshinori Hosoi | ;;;; | Poster | main | https://openaccess.thecvf.com/content/WACV2025/html/Yamaguchi_MDCN-PS_Monocular-Depth-Guided_Coarse_Normal_Attention_for_Robust_Photometric_Stereo_WACV_2025_paper.html | 0 | MDCN-PS: Monocular-Depth-Guided Coarse Normal Attention for Robust Photometric Stereo
Photometric Stereo (PS) is a technique for estimating surface normals from images illuminated by multiple light sources. However when the target object has a complex shape or the light sources are not appropriately arranged certain re... | [
-0.03804316744208336,
-0.02625051699578762,
-0.05421699211001396,
-0.024443283677101135,
0.005873507354408503,
-0.01278754323720932,
0.01083427108824253,
-0.012194259092211723,
0.008976836688816547,
0.019678760319948196,
-0.057356830686330795,
-0.00512505741789937,
0.010359643958508968,
0.... | |||
wacv_2025_68c15f818a | 68c15f818a | wacv | 2,025 | MENTOR: Human Perception-Guided Pretraining for Increased Generalization | Leveraging human perception into training of convolutional neural networks (CNN) has boosted generalization capabilities of such models in open-set recognition tasks. One of the active research questions is where (in the model architecture or training pipeline) and how to efficiently incorporate always-limited human pe... | Colton R. Crum; Adam Czajka | University of Notre Dame; University of Notre Dame | Poster | main | https://openaccess.thecvf.com/content/WACV2025/html/Crum_MENTOR_Human_Perception-Guided_Pretraining_for_Increased_Generalization_WACV_2025_paper.html | 1 | 2310.19545 | MENTOR: Human Perception-Guided Pretraining for Increased Generalization
Leveraging human perception into training of convolutional neural networks (CNN) has boosted generalization capabilities of such models in open-set recognition tasks. One of the active research questions is where (in the model architecture or trai... | [
-0.05969814211130142,
-0.053101662546396255,
-0.035052962601184845,
-0.007581370882689953,
0.004736639093607664,
-0.023417502641677856,
-0.025323152542114258,
0.014622197486460209,
0.01523603592067957,
0.01923973299562931,
-0.049217067658901215,
-0.026972273364663124,
-0.04089817404747009,
... | ||
wacv_2025_29ebafd614 | 29ebafd614 | wacv | 2,025 | MFNeRF: Memory Efficient NeRF with Mixed-Feature Hash Table | Recently neural radiance fields (NeRFs) have shown remarkable performance in generating photorealistic novel views in 3D modeling. The traditional NeRF typically requires extensive training and long rendering times inspiring many recent works to utilize efficient data structures such as feature grids to ease the comput... | Yongjae Lee; Li Yang; Deliang Fan | Arizona State University; University of North Carolina at Charlotte; Arizona State University | Poster | main | https://openaccess.thecvf.com/content/WACV2025/html/Lee_MFNeRF_Memory_Efficient_NeRF_with_Mixed-Feature_Hash_Table_WACV_2025_paper.html | 1 | MFNeRF: Memory Efficient NeRF with Mixed-Feature Hash Table
Recently neural radiance fields (NeRFs) have shown remarkable performance in generating photorealistic novel views in 3D modeling. The traditional NeRF typically requires extensive training and long rendering times inspiring many recent works to utilize effici... | [
0.015116877853870392,
-0.0182103980332613,
-0.016645653173327446,
-0.02194240503013134,
0.0015310223679989576,
-0.040287695825099945,
0.029856059700250626,
0.01706831343472004,
0.023093482479453087,
0.0016940166242420673,
-0.04687041416764259,
-0.02676253952085972,
-0.03924453258514404,
0.... | |||
wacv_2025_b23373e6a4 | b23373e6a4 | wacv | 2,025 | MFTIQ: Multi-Flow Tracker with Independent Matching Quality Estimation | In this work we present MFTIQ a novel dense long-term tracking model that advances the Multi-Flow Tracker (MFT) framework to address challenges in point-level visual tracking in video sequences. MFTIQ builds upon the flow-chaining concepts of MFT integrating an Independent Quality (IQ) module that separates corresponde... | Jonas Serych; Michal Neoral; Jiri Matas | CMP Visual Recognition Group, Faculty of Electrical Engineering, Czech Technical University in Prague; CMP Visual Recognition Group, Faculty of Electrical Engineering, Czech Technical University in Prague; CMP Visual Recognition Group, Faculty of Electrical Engineering, Czech Technical University in Prague | Poster | main | https://github.com/serycjon/MFTIQ | https://openaccess.thecvf.com/content/WACV2025/html/Serych_MFTIQ_Multi-Flow_Tracker_with_Independent_Matching_Quality_Estimation_WACV_2025_paper.html | 3 | 2411.09551 | MFTIQ: Multi-Flow Tracker with Independent Matching Quality Estimation
In this work we present MFTIQ a novel dense long-term tracking model that advances the Multi-Flow Tracker (MFT) framework to address challenges in point-level visual tracking in video sequences. MFTIQ builds upon the flow-chaining concepts of MFT in... | [
-0.03707128018140793,
-0.0442463681101799,
-0.04863114282488823,
0.02565637044608593,
-0.014649135991930962,
-0.02788499742746353,
-0.041782196611166,
0.013389871455729008,
0.015301416628062725,
0.02417062036693096,
-0.010155647061765194,
-0.03989783301949501,
0.013752250000834465,
0.04207... | |
wacv_2025_cda8affe86 | cda8affe86 | wacv | 2,025 | MFTrans: A Multi-Resolution Fusion Transformer for Robust Tumor Segmentation in Whole Slide Images | Accurate tumor segmentation in whole slide image (WSI) is essential for histopathological diagnosis and research but the traditional manual analysis is labor-intensive and prone to variability. Furthermore many artificial models focus on specific magnification images limiting the detailed information available for segm... | Sungkyu Yang; Woohyun Park; Kwangil Yim; Mansu Kim | AI Graduate School, Gwangju Institute of Science and Technology, Gwangju-si, Republic of Korea; Department of Data Science, The Catholic University of Korea, Bucheon-si, Republic of Korea + Department of Hospital Pathology, College of Medicine, The Catholic University of Korea, Seoul, Republic of Korea; Department of H... | Poster | main | https://github.com/aimed-gist/MFTrans | https://openaccess.thecvf.com/content/WACV2025/html/Yang_MFTrans_A_Multi-Resolution_Fusion_Transformer_for_Robust_Tumor_Segmentation_in_WACV_2025_paper.html | 0 | MFTrans: A Multi-Resolution Fusion Transformer for Robust Tumor Segmentation in Whole Slide Images
Accurate tumor segmentation in whole slide image (WSI) is essential for histopathological diagnosis and research but the traditional manual analysis is labor-intensive and prone to variability. Furthermore many artificial... | [
-0.014739989303052425,
-0.014542016200721264,
-0.01487497054040432,
0.017223650589585304,
0.025610506534576416,
0.0018166272202506661,
-0.009043766185641289,
-0.0007648956379853189,
0.04078243672847748,
0.05996781960129738,
-0.0026883832179009914,
-0.008206880651414394,
-0.006425123661756515... | ||
wacv_2025_69a563d9c5 | 69a563d9c5 | wacv | 2,025 | MIP-GAF: A MLLM-Annotated Benchmark for Most Important Person Localization and Group Context Understanding | Estimating the Most Important Person (MIP) in any social event setup is a challenging problem mainly due to contextual complexity and scarcity of labeled data. Moreover the causality aspects of MIP estimation are quite subjective and diverse. To this end we aim to address the problem by annotating a large-scale 'in-the... | S. Madan; S. Ghosh; L. R. Sookha; M.A. Ganaie; R. Subramanian; A. Dhall; T. Gedeon | IIT Ropar; Curtin University; IIT Ropar + University of Canberra; IIT Ropar; IIT Ropar + University of Canberra; Monash University; Curtin University | Poster | main | https://openaccess.thecvf.com/content/WACV2025/html/Madan_MIP-GAF_A_MLLM-Annotated_Benchmark_for_Most_Important_Person_Localization_and_WACV_2025_paper.html | 0 | MIP-GAF: A MLLM-Annotated Benchmark for Most Important Person Localization and Group Context Understanding
Estimating the Most Important Person (MIP) in any social event setup is a challenging problem mainly due to contextual complexity and scarcity of labeled data. Moreover the causality aspects of MIP estimation are ... | [
-0.04604518041014671,
-0.04256246238946915,
-0.017166126519441605,
-0.01218950841575861,
-0.04399221017956734,
-0.015241467393934727,
0.015177312307059765,
-0.011511295102536678,
0.030116332694888115,
-0.00035314058186486363,
-0.024030745029449463,
-0.027531791478395462,
-0.04061947390437126... | |||
wacv_2025_249ba84563 | 249ba84563 | wacv | 2,025 | MLLM-LLaVA-FL: Multimodal Large Language Model Assisted Federated Learning | Previous studies on federated learning (FL) often encounter performance degradation due to data heterogeneity among different clients. In light of the recent advances in multimodal large language models (MLLMs) such as GPT-4v and LLaVA which demonstrate their exceptional proficiency in multimodal tasks such as image ca... | Jianyi Zhang; Hao Yang; Ang Li; Xin Guo; Pu Wang; Haiming Wang; Yiran Chen; Hai Li | ;;;;;;; | Poster | main | https://openaccess.thecvf.com/content/WACV2025/html/Zhang_MLLM-LLaVA-FL_Multimodal_Large_Language_Model_Assisted_Federated_Learning_WACV_2025_paper.html | 2 | MLLM-LLaVA-FL: Multimodal Large Language Model Assisted Federated Learning
Previous studies on federated learning (FL) often encounter performance degradation due to data heterogeneity among different clients. In light of the recent advances in multimodal large language models (MLLMs) such as GPT-4v and LLaVA which dem... | [
-0.018372949212789536,
-0.03390387445688248,
-0.017990369349718094,
-0.034395765513181686,
-0.018873946741223335,
0.02113299071788788,
0.011805325746536255,
0.022171422839164734,
-0.011541163548827171,
-0.02033139392733574,
-0.035397760570049286,
-0.020695757120847702,
-0.01911078207194805,
... | |||
wacv_2025_8d7c6f725a | 8d7c6f725a | wacv | 2,025 | MLLM-Tool: A Multimodal Large Language Model for Tool Agent Learning | Recently the astonishing performance of large language models (LLMs) in natural language comprehension and generation tasks triggered lots of exploration of using them as central controllers to build agent systems. Multiple studies focus on bridging the LLMs to external tools to extend the application scenarios. Howeve... | Chenyu Wang; Weixin Luo; Sixun Dong; Xiaohua Xuan; Zhengxin Li; Lin Ma; Shenghua Gao | ShanghaiTech University; Meituan; ShanghaiTech University; UniDT Technology; ShanghaiTech University; Meituan; University of Hong Kong | Poster | main | github.com/MLLM-Tool/MLLM-Tool | https://openaccess.thecvf.com/content/WACV2025/html/Wang_MLLM-Tool_A_Multimodal_Large_Language_Model_for_Tool_Agent_Learning_WACV_2025_paper.html | 13 | MLLM-Tool: A Multimodal Large Language Model for Tool Agent Learning
Recently the astonishing performance of large language models (LLMs) in natural language comprehension and generation tasks triggered lots of exploration of using them as central controllers to build agent systems. Multiple studies focus on bridging t... | [
-0.04367674142122269,
-0.005545605905354023,
-0.030186204239726067,
-0.0011481668334454298,
-0.04175728186964989,
-0.019828371703624725,
0.005898714065551758,
-0.01762823574244976,
-0.0033183095511049032,
-0.0017791209975257516,
-0.007949456572532654,
-0.04555998370051384,
0.0046809441410005... | ||
wacv_2025_87e6a82612 | 87e6a82612 | wacv | 2,025 | MONAS-ESNN: Multi-Objective Neural Architecture Search for Efficient Spiking Neural Networks | Spiking Neural Networks (SNNs) have emerged as a compelling alternative to traditional Artificial Neural Networks (ANNs) due to their energy efficiency and biological plausibility. However current SNN models often rely on ANN architectures that may not fully exploit the unique properties of SNNs. Neural Architecture Se... | Esmat Ghasemi Saghand; Susana K. Lai-Yuen | University of South Florida; University of South Florida | Poster | main | https://openaccess.thecvf.com/content/WACV2025/html/Saghand_MONAS-ESNN_Multi-Objective_Neural_Architecture_Search_for_Efficient_Spiking_Neural_Networks_WACV_2025_paper.html | 0 | MONAS-ESNN: Multi-Objective Neural Architecture Search for Efficient Spiking Neural Networks
Spiking Neural Networks (SNNs) have emerged as a compelling alternative to traditional Artificial Neural Networks (ANNs) due to their energy efficiency and biological plausibility. However current SNN models often rely on ANN a... | [
-0.030866634100675583,
-0.02189825475215912,
-0.0371573381125927,
0.02820729836821556,
-0.03592854365706444,
-0.03548837825655937,
0.012498877942562103,
-0.016469541937112808,
0.04383318871259689,
0.035561736673116684,
-0.03745078295469284,
0.0024163273628801107,
-0.04207252338528633,
0.00... | |||
wacv_2025_3dfb5a50b7 | 3dfb5a50b7 | wacv | 2,025 | MOOSS: Mask-Enhanced Temporal Contrastive Learning for Smooth State Evolution in Visual Reinforcement Learning | In visual Reinforcement Learning (RL) learning from pixel-based observations poses significant challenges on sample efficiency primarily due to the complexity of extracting informative state representations from high-dimensional data. Previous methods such as contrastive-based approaches have made strides in improving ... | Jiarui Sun; M. Ugur Akcal; Girish Chowdhary; Wei Zhang | University of Illinois Urbana-Champaign; University of Illinois Urbana-Champaign; University of Illinois Urbana-Champaign; Visa Research | Poster | main | https://openaccess.thecvf.com/content/WACV2025/html/Sun_MOOSS_Mask-Enhanced_Temporal_Contrastive_Learning_for_Smooth_State_Evolution_in_WACV_2025_paper.html | 2 | 2409.02714 | MOOSS: Mask-Enhanced Temporal Contrastive Learning for Smooth State Evolution in Visual Reinforcement Learning
In visual Reinforcement Learning (RL) learning from pixel-based observations poses significant challenges on sample efficiency primarily due to the complexity of extracting informative state representations fr... | [
-0.0641554519534111,
-0.02021767757833004,
-0.01742871291935444,
-0.015195688232779503,
0.0017732164124026895,
-0.019216986373066902,
-0.020440055057406425,
0.024146320298314095,
0.019995301961898804,
0.04558706656098366,
-0.0401017926633358,
-0.026499798521399498,
-0.004753285553306341,
0... | ||
wacv_2025_08acab1437 | 08acab1437 | wacv | 2,025 | MRI Reconstruction with Regularized 3D Diffusion Model (R3DM) | Magnetic Resonance Imaging (MRI) is a powerful imaging technique widely used for visualizing structures within the human body and in other fields such as plant sciences. However there is a demand to develop fast 3D-MRI reconstruction algorithms to show the fine structure of objects from under-sampled acquisition data i... | Arya Bangun; Zhuo Cao; Alessio Quercia; Hanno Scharr; Elisabeth Pfaehler | IAS-8; IAS-8; IAS-8+RWTH Aachen University; IAS-8; IAS-8+INM-4 Forschungszentrum Juelich | Poster | main | https://openaccess.thecvf.com/content/WACV2025/html/Bangun_MRI_Reconstruction_with_Regularized_3D_Diffusion_Model_R3DM_WACV_2025_paper.html | 0 | 2412.18723 | MRI Reconstruction with Regularized 3D Diffusion Model (R3DM)
Magnetic Resonance Imaging (MRI) is a powerful imaging technique widely used for visualizing structures within the human body and in other fields such as plant sciences. However there is a demand to develop fast 3D-MRI reconstruction algorithms to show the f... | [
-0.04704506695270538,
0.02525871992111206,
-0.020778238773345947,
-0.02632283605635166,
-0.020404864102602005,
0.028245709836483,
0.002351086586713791,
0.009987742640078068,
0.016885818913578987,
0.039092209190130234,
-0.07082895934581757,
-0.005455920938402414,
0.004867857787758112,
0.048... | ||
wacv_2025_7ad5d86452 | 7ad5d86452 | wacv | 2,025 | MS-Glance: Bio-Inspired Non-Semantic Context Vectors and their Applications in Supervising Image Reconstruction | Non-semantic context information is crucial for visual recognition as the human visual perception system first uses global statistics to process scenes rapidly before identifying specific objects. However while semantic information is increasingly incorporated into computer vision tasks such as image reconstruction non... | Ziqi Gao; Wendi Yang; Yujia Li; Lei Xing; S. Kevin Zhou | 1School of Biomedical Engineering, Division of Life Sciences and Medicine, University of Science and Technology of China (USTC), Hefei Anhui, 230026, China+2Center for Medical Imaging, Robotics, Analytic Computing & Learning (MIRACLE), Suzhou Institute for Advance Research, USTC, Suzhou Jiangsu, 215123, China+3Key Labo... | Poster | main | https://github.com/Z7Gao/MSGlance | https://openaccess.thecvf.com/content/WACV2025/html/Gao_MS-Glance_Bio-Inspired_Non-Semantic_Context_Vectors_and_their_Applications_in_Supervising_WACV_2025_paper.html | 0 | MS-Glance: Bio-Inspired Non-Semantic Context Vectors and their Applications in Supervising Image Reconstruction
Non-semantic context information is crucial for visual recognition as the human visual perception system first uses global statistics to process scenes rapidly before identifying specific objects. However whi... | [
-0.087055504322052,
-0.0012521962635219097,
-0.036485426127910614,
0.013866282068192959,
0.009735513478517532,
-0.02573087066411972,
-0.015776989981532097,
0.005272644571959972,
0.02986164018511772,
0.014976312406361103,
-0.014985411427915096,
-0.033355504274368286,
-0.010463401675224304,
... | ||
wacv_2025_e7d994e694 | e7d994e694 | wacv | 2,025 | MSI-NeRF: Linking Omni-Depth with View Synthesis through Multi-Sphere Image Aided Generalizable Neural Radiance Field | Panoramic observation using fisheye cameras is significant in virtual reality (VR) and robot perception. However panoramic images synthesized by traditional methods lack depth information and can only provide three degrees-of-freedom (3DoF) rotation rendering in VR applications. To fully preserve and exploit the parall... | Dongyu Yan; Guanyu Huang; Fengyu Quan; Haoyao Chen | ;;; | Poster | main | https://openaccess.thecvf.com/content/WACV2025/html/Yan_MSI-NeRF_Linking_Omni-Depth_with_View_Synthesis_through_Multi-Sphere_Image_Aided_WACV_2025_paper.html | 1 | MSI-NeRF: Linking Omni-Depth with View Synthesis through Multi-Sphere Image Aided Generalizable Neural Radiance Field
Panoramic observation using fisheye cameras is significant in virtual reality (VR) and robot perception. However panoramic images synthesized by traditional methods lack depth information and can only p... | [
-0.06142684817314148,
-0.013178783468902111,
0.0013206587173044682,
0.0041473316960036755,
0.006246483884751797,
0.001238407101482153,
0.03230748325586319,
0.011315959505736828,
0.05419797822833061,
0.025838570669293404,
-0.029286185279488564,
-0.021482713520526886,
-0.028377942740917206,
... | |||
wacv_2025_b9c61c3b07 | b9c61c3b07 | wacv | 2,025 | MVAD: A Multiple Visual Artifact Detector for Video Streaming | Visual artifacts are often introduced into streamed video content due to prevailing conditions during content production and delivery. Since these can degrade the quality of the user's experience it is important to automatically and accurately detect them in order to enable effective quality measurement and enhancement... | Chen Feng; Duolikun Danier; Fan Zhang; Alex Mackin; Andrew Collins; David Bull | ;;;;; | Poster | main | https://openaccess.thecvf.com/content/WACV2025/html/Feng_MVAD_A_Multiple_Visual_Artifact_Detector_for_Video_Streaming_WACV_2025_paper.html | 1 | 2406.00212 | MVAD: A Multiple Visual Artifact Detector for Video Streaming
Visual artifacts are often introduced into streamed video content due to prevailing conditions during content production and delivery. Since these can degrade the quality of the user's experience it is important to automatically and accurately detect them in... | [
-0.009998220019042492,
-0.03692905232310295,
-0.03745479881763458,
0.028879716992378235,
0.016434064134955406,
0.017231745645403862,
-0.01073244959115982,
0.03618576005101204,
0.01182019803673029,
0.019887663424015045,
-0.01437640655785799,
-0.07606986165046692,
-0.032215479761362076,
0.01... | ||
wacv_2025_3850c3972a | 3850c3972a | wacv | 2,025 | MVFNet: Multipurpose Video Forensics Network using Multiple Forms of Forensic Evidence | While videos can be falsified in many different ways most existing forensic networks are specialized to detect only a single manipulation type (e.g. deepfake inpainting). This poses a significant issue as the manipulation used to falsify a video is not known a priori. To address this problem we propose MVFNet - a multi... | Tai D Nguyen; Matthew C Stamm | ; | Poster | main | https://openaccess.thecvf.com/content/WACV2025/html/Nguyen_MVFNet_Multipurpose_Video_Forensics_Network_using_Multiple_Forms_of_Forensic_WACV_2025_paper.html | 0 | MVFNet: Multipurpose Video Forensics Network using Multiple Forms of Forensic Evidence
While videos can be falsified in many different ways most existing forensic networks are specialized to detect only a single manipulation type (e.g. deepfake inpainting). This poses a significant issue as the manipulation used to fal... | [
-0.03361312299966812,
-0.03867257013916969,
-0.033392347395420074,
0.009359976276755333,
-0.010376465506851673,
0.024101365357637405,
0.014304435811936855,
0.036703985184431076,
0.023199863731861115,
0.011185976676642895,
-0.033263564109802246,
-0.005386011209338903,
-0.00932318065315485,
... | |||
wacv_2025_3accab7be0 | 3accab7be0 | wacv | 2,025 | MVMD: A Multi-View Approach for Enhanced Mirror Detection | In 3D reconstruction mirrors introduce significant challenges by creating distorted and fragmented spaces resulting in inaccurate and unreliable 3D models. As 3D reconstruction typically relies on multi-view images to capture different perspectives of a scene detecting and labeling mirrors in multi-view images before r... | Yidan Shen; Yu Wen; Chen Zhang; Xin Fu; Renjie Hu | University of Houston; University of Houston; University of Houston; University of Houston; University of Houston | Poster | main | https://github.com/mvmdwacv25 | https://openaccess.thecvf.com/content/WACV2025/html/Shen_MVMD_A_Multi-View_Approach_for_Enhanced_Mirror_Detection_WACV_2025_paper.html | 0 | MVMD: A Multi-View Approach for Enhanced Mirror Detection
In 3D reconstruction mirrors introduce significant challenges by creating distorted and fragmented spaces resulting in inaccurate and unreliable 3D models. As 3D reconstruction typically relies on multi-view images to capture different perspectives of a scene de... | [
-0.01992151141166687,
-0.008010108023881912,
-0.05530064180493355,
0.014203299768269062,
-0.005141778849065304,
0.04006437212228775,
-0.009693291038274765,
0.009877749718725681,
0.03440149873495102,
0.0010300851427018642,
-0.030638547614216805,
-0.022264134138822556,
-0.018621079623699188,
... | ||
wacv_2025_c21c2a3f20 | c21c2a3f20 | wacv | 2,025 | MagicStick: Controllable Video Editing via Control Handle Transformations | Text-based video editing has recently attracted considerable interest in changing the style or replacing the objects with a similar structure. Beyond this we demonstrate that properties such as shape size location motion etc. can also be edited in videos. Our key insight is that the keyframe's transformations of the sp... | Yue Ma; Xiaodong Cun; Sen Liang; Jinbo Xing; Yingqing He; Chenyang Qi; Siran Chen; Qifeng Chen | HKUST; Great Bay University; USTC; SIAT@MMLab; CUHK; HKUST; SIAT@MMLab; HKUST | Poster | main | https://openaccess.thecvf.com/content/WACV2025/html/Ma_MagicStick_Controllable_Video_Editing_via_Control_Handle_Transformations_WACV_2025_paper.html | 30 | 2312.03047 | MagicStick: Controllable Video Editing via Control Handle Transformations
Text-based video editing has recently attracted considerable interest in changing the style or replacing the objects with a similar structure. Beyond this we demonstrate that properties such as shape size location motion etc. can also be edited i... | [
-0.047300565987825394,
-0.0007768087089061737,
-0.006581392604857683,
-0.009409951977431774,
-0.010905511677265167,
-0.008764352649450302,
-0.004679430741816759,
0.011797274462878704,
-0.0010281974682584405,
0.017575152218341827,
-0.01823468506336212,
-0.055809468030929565,
0.000589573406614... | ||
wacv_2025_4ec39d0583 | 4ec39d0583 | wacv | 2,025 | Make VLM Recognize Visual Hallucination on Cartoon Character Image with Pose Information | Leveraging large-scale Text-to-Image (TTI) models have become a common technique to generate training or reference data in the field of image synthesis video editing 3D reconstruction. However semantic structural visual hallucinations which contain perceptually critical defects remain a concern especially in non-photor... | Bumsoo Kim; Wonseop Shin; Kyuchul Lee; Yonghoon Jung; Sanghyun Seo | Chung-Ang University; Chung-Ang University; Coupang; Chung-Ang University; Chung-Ang University | Poster | main | https://openaccess.thecvf.com/content/WACV2025/html/Kim_Make_VLM_Recognize_Visual_Hallucination_on_Cartoon_Character_Image_with_WACV_2025_paper.html | 0 | 2403.15048 | Make VLM Recognize Visual Hallucination on Cartoon Character Image with Pose Information
Leveraging large-scale Text-to-Image (TTI) models have become a common technique to generate training or reference data in the field of image synthesis video editing 3D reconstruction. However semantic structural visual hallucinati... | [
-0.05553484708070755,
-0.031622495502233505,
0.010019548237323761,
0.02893121913075447,
-0.008023821748793125,
-0.004439241718500853,
0.022203028202056885,
0.01852070912718773,
0.007028231397271156,
-0.00020798291370738298,
-0.028567533940076828,
-0.03498658910393715,
-0.0025776242837309837,... | ||
wacv_2025_e8f344b610 | e8f344b610 | wacv | 2,025 | Make-A-Texture: Fast Shape-Aware 3D Texture Generation in 3 Seconds | We present Make-A-Texture a new framework that efficiently synthesizes high-resolution texture maps from textual prompts for given 3D geometries. Our approach progressively generates textures that are consistent across multiple viewpoints with a depth-aware inpainting diffusion model in an optimized sequence of viewpoi... | Liat Sless Gorelik; Yuchen Fan; Omri Armstrong; Forrest N Iandola; Yilei Li; Ita Lifshitz; Rakesh Ranjan | ;;;;;; | Poster | main | https://openaccess.thecvf.com/content/WACV2025/html/Gorelik_Make-A-Texture_Fast_Shape-Aware_3D_Texture_Generation_in_3_Seconds_WACV_2025_paper.html | 3 | Make-A-Texture: Fast Shape-Aware 3D Texture Generation in 3 Seconds
We present Make-A-Texture a new framework that efficiently synthesizes high-resolution texture maps from textual prompts for given 3D geometries. Our approach progressively generates textures that are consistent across multiple viewpoints with a depth-... | [
-0.011041248217225075,
-0.013039538636803627,
-0.0014296527951955795,
-0.0025531158316880465,
-0.005387097597122192,
0.006920348387211561,
-0.015268047340214252,
0.04221274331212044,
0.028031324967741966,
0.03386965021491051,
-0.007974746637046337,
0.027313046157360077,
0.049948062747716904,... | |||
wacv_2025_e97df8d6cd | e97df8d6cd | wacv | 2,025 | Mamba-ST: State Space Model for Efficient Style Transfer | The goal of style transfer is given a content image and a style source generating a new image preserving the content but with the artistic representation of the style source. Most of the state-of-the-art architectures use transformers or diffusion-based models to perform this task despite the heavy computational burden... | Filippo Botti; Alex Ergasti; Leonardo Rossi; Tomaso Fontanini; Claudio Ferrari; Massimo Bertozzi; Andrea Prati | University of Parma, Department of Engineering and Architecture; University of Parma, Department of Engineering and Architecture; University of Parma, Department of Engineering and Architecture; University of Parma, Department of Engineering and Architecture; University of Parma, Department of Engineering and Architect... | Poster | main | https://github.com/FilippoBotti/MambaST | https://openaccess.thecvf.com/content/WACV2025/html/Botti_Mamba-ST_State_Space_Model_for_Efficient_Style_Transfer_WACV_2025_paper.html | 2 | Mamba-ST: State Space Model for Efficient Style Transfer
The goal of style transfer is given a content image and a style source generating a new image preserving the content but with the artistic representation of the style source. Most of the state-of-the-art architectures use transformers or diffusion-based models to... | [
-0.029533658176660538,
-0.03288019821047783,
-0.06115204840898514,
0.010460218414664268,
0.005870157387107611,
-0.014794260263442993,
-0.0007074819877743721,
0.009399566799402237,
0.04132883623242378,
0.01622065342962742,
-0.019420895725488663,
0.01234378945082426,
-0.02130446583032608,
0.... | ||
wacv_2025_d541217ea7 | d541217ea7 | wacv | 2,025 | MambaRecon: MRI Reconstruction with Structured State Space Models | Magnetic Resonance Imaging (MRI) is one of the most important medical imaging modalities as it provides superior resolution of soft tissues albeit with a notable limitation in scanning speed. The advent of deep learning has catalyzed the development of cutting-edge methods for the expedited reconstruction of MRI scans ... | Yilmaz Korkmaz; Vishal M. Patel | Johns Hopkins University; Johns Hopkins University | Poster | main | https://github.com/yilmazkorkmaz1/MambaRecon | https://openaccess.thecvf.com/content/WACV2025/html/Korkmaz_MambaRecon_MRI_Reconstruction_with_Structured_State_Space_Models_WACV_2025_paper.html | 6 | 2409.12401 | MambaRecon: MRI Reconstruction with Structured State Space Models
Magnetic Resonance Imaging (MRI) is one of the most important medical imaging modalities as it provides superior resolution of soft tissues albeit with a notable limitation in scanning speed. The advent of deep learning has catalyzed the development of c... | [
0.005996476858854294,
0.01010578591376543,
-0.010266195051372051,
-0.020739978179335594,
-0.0181451216340065,
0.022872479632496834,
0.0340256430208683,
0.029477568343281746,
0.06718318909406662,
0.021664690226316452,
-0.032855600118637085,
-0.016050364822149277,
-0.0017798355547711253,
0.0... | |
wacv_2025_49d90a3e66 | 49d90a3e66 | wacv | 2,025 | MaskVD: Region Masking for Efficient Video Object Detection | Video tasks are compute-heavy and thus pose a challenge when deploying in real-time applications particularly for tasks that require state-of-the-art Vision Transformers (ViTs). Several research efforts have tried to address this challenge by leveraging the fact that large portions of the video undergo very little chan... | Sreetama Sarkar; Gourav Datta; Souvik Kundu; Kai Zheng; Chirayata Bhattacharyya; Peter A. Beerel | Universiy of Southern California, Los Angeles, USA; Case Western Reserve University, USA; Intel Labs, San Diego, USA; Indian Institute of Science, Bangalore, India; Indian Institute of Science, Bangalore, India; Universiy of Southern California, Los Angeles, USA | Poster | main | https://github.com/sreetamasarkar/MaskVD | https://openaccess.thecvf.com/content/WACV2025/html/Sarkar_MaskVD_Region_Masking_for_Efficient_Video_Object_Detection_WACV_2025_paper.html | 4 | 2407.12067 | MaskVD: Region Masking for Efficient Video Object Detection
Video tasks are compute-heavy and thus pose a challenge when deploying in real-time applications particularly for tasks that require state-of-the-art Vision Transformers (ViTs). Several research efforts have tried to address this challenge by leveraging the fa... | [
-0.015617414377629757,
-0.03211675211787224,
-0.043434783816337585,
0.009218867868185043,
-0.006306679453700781,
0.03727968409657478,
0.026237254962325096,
0.011501765809953213,
0.006563907023519278,
-0.005567148793488741,
-0.024675514549016953,
-0.02575954608619213,
0.0077857403084635735,
... | |
wacv_2025_4f598f49b9 | 4f598f49b9 | wacv | 2,025 | MatSpectNet: Material Segmentation Network with Domain-Aware and Physically-Constrained Hyperspectral Reconstruction | Achieving accurate material segmentation for 3-channel RGB images is challenging due to the considerable variation in material appearance. Hyperspectral images which are sets of spectral measurements sampled at multiple wavelengths theoretically offer distinct information for material identification as variations in th... | Yuwen Heng; Yihong Wu; Srinandan Dasmahapatra; Hansung Kim | School of Electronics and Computer Science, University of Southampton, UK+Bosch Corporate Research; School of Electronics and Computer Science, University of Southampton, UK; School of Electronics and Computer Science, University of Southampton, UK; School of Electronics and Computer Science, University of Southampton,... | Poster | main | https://github.com/heng-yuwen/MatSpectNet | https://openaccess.thecvf.com/content/WACV2025/html/Heng_MatSpectNet_Material_Segmentation_Network_with_Domain-Aware_and_Physically-Constrained_Hyperspectral_Reconstruction_WACV_2025_paper.html | 1 | 2307.11466 | MatSpectNet: Material Segmentation Network with Domain-Aware and Physically-Constrained Hyperspectral Reconstruction
Achieving accurate material segmentation for 3-channel RGB images is challenging due to the considerable variation in material appearance. Hyperspectral images which are sets of spectral measurements sam... | [
-0.04247032105922699,
-0.025460148230195045,
-0.021492334082722664,
-0.017653103917837143,
-0.0027186882216483355,
-0.014208820648491383,
0.015981478616595268,
0.013575072400271893,
0.0019184662960469723,
0.02827068418264389,
-0.04309488460421562,
0.0004133140901103616,
-0.016716258600354195... | |
wacv_2025_719ac5e177 | 719ac5e177 | wacv | 2,025 | McCaD: Multi-Contrast MRI Conditioned Adaptive Adversarial Diffusion Model for High-Fidelity MRI Synthesis | Magnetic Resonance Imaging (MRI) is instrumental in clinical diagnosis offering diverse contrasts that provide comprehensive diagnostic information. However acquiring multiple MRI contrasts is often constrained by high costs long scanning durations and patient discomfort. Current synthesis methods typically focused on ... | Sanuwani Dayarathna; Kh Tohidul Islam; Bohan Zhuang; Guang Yang; Jianfei Cai; Meng Law; Zhaolin Chen | ;;;;;; | Poster | main | https://openaccess.thecvf.com/content/WACV2025/html/Dayarathna_McCaD_Multi-Contrast_MRI_Conditioned_Adaptive_Adversarial_Diffusion_Model_for_High-Fidelity_WACV_2025_paper.html | 1 | 2409.00585 | McCaD: Multi-Contrast MRI Conditioned Adaptive Adversarial Diffusion Model for High-Fidelity MRI Synthesis
Magnetic Resonance Imaging (MRI) is instrumental in clinical diagnosis offering diverse contrasts that provide comprehensive diagnostic information. However acquiring multiple MRI contrasts is often constrained by... | [
-0.018175652250647545,
-0.024901486933231354,
-0.052085451781749725,
-0.021234551444649696,
0.0036458882968872786,
-0.0029536604415625334,
0.006211339961737394,
0.01222623698413372,
0.04119689762592316,
0.029934167861938477,
-0.0682499036192894,
-0.022282246500253677,
-0.0016475485172122717,... | ||
wacv_2025_8dca6f2273 | 8dca6f2273 | wacv | 2,025 | MegaFusion: Extend Diffusion Models towards Higher-Resolution Image Generation without Further Tuning | Diffusion models have emerged as frontrunners in text-to-image generation but their fixed image resolution during training often leads to challenges in high-resolution image generation such as semantic deviations and object replication. This paper introduces MegaFusion a novel approach that extends existing diffusion-b... | Haoning Wu; Shaocheng Shen; Qiang Hu; Xiaoyun Zhang; Ya Zhang; Yanfeng Wang | Shanghai Jiao Tong University; Shanghai Jiao Tong University; Shanghai Jiao Tong University; Shanghai Jiao Tong University; Shanghai Jiao Tong University; Shanghai Jiao Tong University | Poster | main | https://haoningwu3639.github.io/MegaFusion/ | https://openaccess.thecvf.com/content/WACV2025/html/Wu_MegaFusion_Extend_Diffusion_Models_towards_Higher-Resolution_Image_Generation_without_Further_WACV_2025_paper.html | 8 | 2408.11001 | MegaFusion: Extend Diffusion Models towards Higher-Resolution Image Generation without Further Tuning
Diffusion models have emerged as frontrunners in text-to-image generation but their fixed image resolution during training often leads to challenges in high-resolution image generation such as semantic deviations and o... | [
-0.03809288516640663,
-0.014562251046299934,
-0.020629096776247025,
0.022320901975035667,
-0.022666538134217262,
0.007517613936215639,
-0.012652149423956871,
0.011606140993535519,
0.01810048706829548,
0.008013331331312656,
-0.041767556220293045,
-0.04191308468580246,
-0.0103782182559371,
0... | |
wacv_2025_fe25066e0a | fe25066e0a | wacv | 2,025 | MemControl: Mitigating Memorization in Diffusion Models via Automated Parameter Selection | Diffusion models excel in generating images that closely resemble their training data but are also susceptible to data memorization raising privacy ethical and legal concerns particularly in sensitive domains such as medical imaging. We hypothesize that this memorization stems from the overparameterization of deep mode... | Raman Dutt; Ondrej Bohdal; Pedro Sanchez; Sotirios Tsaftaris; Timothy Hospedales | The University of Edinburgh; The University of Edinburgh; The University of Edinburgh; The University of Edinburgh; The University of Edinburgh+Samsung AI Center, Cambridge | Poster | main | https://openaccess.thecvf.com/content/WACV2025/html/Dutt_MemControl_Mitigating_Memorization_in_Diffusion_Models_via_Automated_Parameter_Selection_WACV_2025_paper.html | 1 | 2405.19458 | MemControl: Mitigating Memorization in Diffusion Models via Automated Parameter Selection
Diffusion models excel in generating images that closely resemble their training data but are also susceptible to data memorization raising privacy ethical and legal concerns particularly in sensitive domains such as medical imagi... | [
-0.03401976078748703,
-0.039086535573005676,
-0.01592414267361164,
0.011174043640494347,
-0.019289927557110786,
-0.04853244498372078,
-0.003524121595546603,
0.039557021111249924,
0.02337953820824623,
0.036951251327991486,
-0.07672541588544846,
-0.023343347012996674,
-0.004994390532374382,
... | ||
wacv_2025_517e46eead | 517e46eead | wacv | 2,025 | MemFusionMap: Working Memory Fusion for Online Vectorized HD Map Construction | High-definition (HD) maps provide environmental information for autonomous driving systems and are essential for safe planning. While existing methods with single-frame input achieve impressive performance for online vectorized HD map construction they still struggle with complex scenarios and occlusions. We propose Me... | Jingyu Song; Xudong Chen; Liupei Lu; Jie Li; Katherine A. Skinner | University of Michigan; NVIDIA; NVIDIA; NVIDIA; University of Michigan | Poster | main | https://openaccess.thecvf.com/content/WACV2025/html/Song_MemFusionMap_Working_Memory_Fusion_for_Online_Vectorized_HD_Map_Construction_WACV_2025_paper.html | 1 | 2409.18737 | MemFusionMap: Working Memory Fusion for Online Vectorized HD Map Construction
High-definition (HD) maps provide environmental information for autonomous driving systems and are essential for safe planning. While existing methods with single-frame input achieve impressive performance for online vectorized HD map constru... | [
-0.030050935223698616,
-0.009116369299590588,
-0.019776640459895134,
0.020566970109939575,
-0.021485958248376846,
-0.028341615572571754,
-0.020842665806412697,
0.012580957263708115,
0.030841264873743057,
0.053632188588380814,
-0.009456395171582699,
-0.043670348823070526,
0.02777184173464775,... | ||
wacv_2025_dfe54f6a98 | dfe54f6a98 | wacv | 2,025 | Memory-Efficient Continual Learning with Neural Collapse Contrastive | Contrastive learning has significantly improved representation quality enhancing knowledge transfer across tasks in continual learning (CL). However catastrophic forgetting remains a key challenge as contrastive based methods primarily focus on "soft relationships" or "softness" between samples which shift with changin... | Trung-Anh Dang; Vincent Nguyen; Ngoc-Son Vu; Christel Vrain | Université d’Orléans, INSA CVL, LIFO UR 4022, Orléans, France; Université d’Orléans, INSA CVL, LIFO UR 4022, Orléans, France; ETIS - CY Cergy Paris University, ENSEA, CNRS, France; Université d’Orléans, INSA CVL, LIFO UR 4022, Orléans, France | Poster | main | https://openaccess.thecvf.com/content/WACV2025/html/Dang_Memory-Efficient_Continual_Learning_with_Neural_Collapse_Contrastive_WACV_2025_paper.html | 1 | 2412.02865 | Memory-Efficient Continual Learning with Neural Collapse Contrastive
Contrastive learning has significantly improved representation quality enhancing knowledge transfer across tasks in continual learning (CL). However catastrophic forgetting remains a key challenge as contrastive based methods primarily focus on "soft ... | [
-0.08984434604644775,
-0.03719943016767502,
-0.00929520558565855,
-0.006396850571036339,
-0.014515035785734653,
-0.024247553199529648,
0.020618794485926628,
0.03881841525435448,
0.00634567579254508,
0.017706481739878654,
-0.023800937458872795,
-0.03809266537427902,
-0.0249174777418375,
0.0... | ||
wacv_2025_74b391ceba | 74b391ceba | wacv | 2,025 | Memory-Efficient Pseudo-Labeling for Online Source-Free Universal Domain Adaptation using a Gaussian Mixture Model | In practice domain shifts are likely to occur between training and test data necessitating domain adaptation (DA) to adjust the pre-trained source model to the target domain. Recently universal domain adaptation (UniDA) has gained attention for addressing the possibility of an additional category (label) shift between ... | Pascal Schlachter; Simon Wagner; Bin Yang | University of Stuttgart, Germany; University of Stuttgart, Germany; University of Stuttgart, Germany | Poster | main | https://github.com/pascalschlachter/GMM | https://openaccess.thecvf.com/content/WACV2025/html/Schlachter_Memory-Efficient_Pseudo-Labeling_for_Online_Source-Free_Universal_Domain_Adaptation_using_a_WACV_2025_paper.html | 1 | 2407.14208 | Memory-Efficient Pseudo-Labeling for Online Source-Free Universal Domain Adaptation using a Gaussian Mixture Model
In practice domain shifts are likely to occur between training and test data necessitating domain adaptation (DA) to adjust the pre-trained source model to the target domain. Recently universal domain adap... | [
-0.08805357664823532,
-0.01918598636984825,
-0.05001208186149597,
-0.0047368272207677364,
-0.019075827673077583,
-0.049277689307928085,
0.0012392861535772681,
0.010611961595714092,
0.0528394915163517,
0.023427098989486694,
-0.023886093869805336,
0.037141866981983185,
-0.008004870265722275,
... | |
wacv_2025_5561a439e2 | 5561a439e2 | wacv | 2,025 | Meta-Learning for Color-to-Infrared Cross-Modal Style Transfer | Recent object detection models for infrared (IR) imagery are based upon deep neural networks (DNNs) and require large amounts of labeled training imagery. However publicly available datasets that can be used for such training are limited in their size and diversity. To address this problem we explore cross-modal style ... | Evelyn A. Stump; Francesco Luzi; Leslie M. Collins; Jordan M. Malof | Electrical and Computer Engineering, Duke University; Electrical and Computer Engineering, Duke University; Electrical and Computer Engineering, Duke University; Electrical Engineering and Computer Science, University of Missouri | Poster | main | https://openaccess.thecvf.com/content/WACV2025/html/Stump_Meta-Learning_for_Color-to-Infrared_Cross-Modal_Style_Transfer_WACV_2025_paper.html | 4 | 2212.12824 | Meta-Learning for Color-to-Infrared Cross-Modal Style Transfer
Recent object detection models for infrared (IR) imagery are based upon deep neural networks (DNNs) and require large amounts of labeled training imagery. However publicly available datasets that can be used for such training are limited in their size and d... | [
-0.07991914451122284,
-0.05030722916126251,
-0.0328226238489151,
0.01344828587025404,
-0.015448099002242088,
-0.0033116175327450037,
-0.04230797663331032,
-0.014218855649232864,
0.02759375423192978,
0.022566700354218483,
-0.03630853816866875,
-0.00455232709646225,
0.019998133182525635,
0.0... | ||
wacv_2025_ba7e70aff1 | ba7e70aff1 | wacv | 2,025 | MetaVIn: Meteorological and Visual Integration for Atmospheric Turbulence Strength Estimation | Long-range image understanding is a challenging task for computer vision due to the presence of atmospheric turbulence. Turbulence can degrade image quality (blur and geometric distortion) due to the medium's spatio-temporal varying index of refraction bending light rays. The strength of atmospheric turbulence is quant... | Ripon Kumar Saha; Scott McCloskey; Suren Jayasuriya | Arizona State University; Kitware, Inc.; Arizona State University | Poster | main | https://openaccess.thecvf.com/content/WACV2025/html/Saha_MetaVIn_Meteorological_and_Visual_Integration_for_Atmospheric_Turbulence_Strength_Estimation_WACV_2025_paper.html | 0 | MetaVIn: Meteorological and Visual Integration for Atmospheric Turbulence Strength Estimation
Long-range image understanding is a challenging task for computer vision due to the presence of atmospheric turbulence. Turbulence can degrade image quality (blur and geometric distortion) due to the medium's spatio-temporal v... | [
-0.03751847520470619,
0.012524964287877083,
-0.008270049467682838,
-0.0034391935914754868,
0.014593652449548244,
-0.008773117326200008,
-0.009666414000093937,
0.029055660590529442,
0.030973898246884346,
0.023188110440969467,
-0.031669728457927704,
-0.04517262056469917,
-0.02354542911052704,
... |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.