WACV
Collection
Accepted papers for WACV (IEEE/CVF Winter Conference on Applications of Computer Vision), one dataset per year. • 7 items • Updated
paper_id stringlengths 47 129 | title stringlengths 24 164 | authors listlengths 1 12 | cvf_url stringlengths 104 186 | pdf_url stringlengths 105 187 | supp_url stringlengths 103 141 ⌀ | arxiv_id stringlengths 10 10 ⌀ | arxiv_id_source stringclasses 3
values | bibtex large_stringlengths 320 605 | abstract large_stringlengths 500 1.99k |
|---|---|---|---|---|---|---|---|---|---|
Qiu_3D_Change_Localization_and_Captioning_From_Dynamic_Scans_of_Indoor_WACV_2023_paper | 3D Change Localization and Captioning From Dynamic Scans of Indoor Scenes | [
"Yue Qiu",
"Shintaro Yamamoto",
"Ryosuke Yamada",
"Ryota Suzuki",
"Hirokatsu Kataoka",
"Kenji Iwata",
"Yutaka Satoh"
] | https://openaccess.thecvf.com/content/WACV2023/html/Qiu_3D_Change_Localization_and_Captioning_From_Dynamic_Scans_of_Indoor_WACV_2023_paper.html | https://openaccess.thecvf.com/content/WACV2023/papers/Qiu_3D_Change_Localization_and_Captioning_From_Dynamic_Scans_of_Indoor_WACV_2023_paper.pdf | https://openaccess.thecvf.com/content/WACV2023/supplemental/Qiu_3D_Change_Localization_WACV_2023_supplemental.pdf | null | null | @InProceedings{Qiu_2023_WACV,
author = {Qiu, Yue and Yamamoto, Shintaro and Yamada, Ryosuke and Suzuki, Ryota and Kataoka, Hirokatsu and Iwata, Kenji and Satoh, Yutaka},
title = {3D Change Localization and Captioning From Dynamic Scans of Indoor Scenes},
booktitle = {Proceedings of the IEEE/CVF Winte... | Daily indoor scenes often involve constant changes due to human activities. To recognize scene changes, existing change captioning methods focus on describing changes from two images of a scene. However, to accurately perceive and appropriately evaluate physical changes and then identify the geometry of changed objects... |
Zhang_Panoptic-Aware_Image-to-Image_Translation_WACV_2023_paper | Panoptic-Aware Image-to-Image Translation | [
"Liyun Zhang",
"Photchara Ratsamee",
"Bowen Wang",
"Zhaojie Luo",
"Yuki Uranishi",
"Manabu Higashida",
"Haruo Takemura"
] | https://openaccess.thecvf.com/content/WACV2023/html/Zhang_Panoptic-Aware_Image-to-Image_Translation_WACV_2023_paper.html | https://openaccess.thecvf.com/content/WACV2023/papers/Zhang_Panoptic-Aware_Image-to-Image_Translation_WACV_2023_paper.pdf | https://openaccess.thecvf.com/content/WACV2023/supplemental/Zhang_Panoptic-Aware_Image-to-Image_Translation_WACV_2023_supplemental.pdf | 2112.01926 | cvf | @InProceedings{Zhang_2023_WACV,
author = {Zhang, Liyun and Ratsamee, Photchara and Wang, Bowen and Luo, Zhaojie and Uranishi, Yuki and Higashida, Manabu and Takemura, Haruo},
title = {Panoptic-Aware Image-to-Image Translation},
booktitle = {Proceedings of the IEEE/CVF Winter Conference on Application... | Despite remarkable progress in image translation, the complex scene with multiple discrepant objects remains a challenging problem. The translated images have low fidelity and tiny objects in fewer details causing unsatisfactory performance in object recognition. Without thorough object perception (i.e., bounding boxes... |
Bhayani_Partially_Calibrated_Semi-Generalized_Pose_From_Hybrid_Point_Correspondences_WACV_2023_paper | Partially Calibrated Semi-Generalized Pose From Hybrid Point Correspondences | [
"Snehal Bhayani",
"Torsten Sattler",
"Viktor Larsson",
"Janne Heikkilä",
"Zuzana Kukelova"
] | https://openaccess.thecvf.com/content/WACV2023/html/Bhayani_Partially_Calibrated_Semi-Generalized_Pose_From_Hybrid_Point_Correspondences_WACV_2023_paper.html | https://openaccess.thecvf.com/content/WACV2023/papers/Bhayani_Partially_Calibrated_Semi-Generalized_Pose_From_Hybrid_Point_Correspondences_WACV_2023_paper.pdf | https://openaccess.thecvf.com/content/WACV2023/supplemental/Bhayani_Partially_Calibrated_Semi-Generalized_WACV_2023_supplemental.pdf | 2209.15072 | cvf | @InProceedings{Bhayani_2023_WACV,
author = {Bhayani, Snehal and Sattler, Torsten and Larsson, Viktor and Heikkil\"a, Janne and Kukelova, Zuzana},
title = {Partially Calibrated Semi-Generalized Pose From Hybrid Point Correspondences},
booktitle = {Proceedings of the IEEE/CVF Winter Conference on Appli... | In this paper we study the problem of estimating the semi-generalized pose of a partially calibrated camera, i.e., the pose of a perspective camera with unknown focal length w.r.t. a generalized camera, from a hybrid set of 2D-2D and 2D-3D point correspondences. We study all possible camera configurations within the ge... |
Kayabasi_Elimination_of_Non-Novel_Segments_at_Multi-Scale_for_Few-Shot_Segmentation_WACV_2023_paper | Elimination of Non-Novel Segments at Multi-Scale for Few-Shot Segmentation | [
"Alper Kayabaşı",
"Gülin Tüfekci",
"İlkay Ulusoy"
] | https://openaccess.thecvf.com/content/WACV2023/html/Kayabasi_Elimination_of_Non-Novel_Segments_at_Multi-Scale_for_Few-Shot_Segmentation_WACV_2023_paper.html | https://openaccess.thecvf.com/content/WACV2023/papers/Kayabasi_Elimination_of_Non-Novel_Segments_at_Multi-Scale_for_Few-Shot_Segmentation_WACV_2023_paper.pdf | null | 2211.02300 | title_snapshot | @InProceedings{Kayabasi_2023_WACV,
author = {Kayaba\c{s}{\i}, Alper and T\"ufekci, G\"ulin and Ulusoy, \.Ilkay},
title = {Elimination of Non-Novel Segments at Multi-Scale for Few-Shot Segmentation},
booktitle = {Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)},... | Few-shot segmentation aims to devise a generalizing model that segments query images from unseen classes during training with the guidance of a few support images whose class tally with the class of the query. There exist two domain-specific problems mentioned in the previous works, namely spatial inconsistency and bia... |
Chandra_Continual_Learning_With_Dependency_Preserving_Hypernetworks_WACV_2023_paper | Continual Learning With Dependency Preserving Hypernetworks | [
"Dupati Srikar Chandra",
"Sakshi Varshney",
"P. K. Srijith",
"Sunil Gupta"
] | https://openaccess.thecvf.com/content/WACV2023/html/Chandra_Continual_Learning_With_Dependency_Preserving_Hypernetworks_WACV_2023_paper.html | https://openaccess.thecvf.com/content/WACV2023/papers/Chandra_Continual_Learning_With_Dependency_Preserving_Hypernetworks_WACV_2023_paper.pdf | null | 2209.07712 | cvf | @InProceedings{Chandra_2023_WACV,
author = {Chandra, Dupati Srikar and Varshney, Sakshi and Srijith, P. K. and Gupta, Sunil},
title = {Continual Learning With Dependency Preserving Hypernetworks},
booktitle = {Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)},
... | Humans learn continually throughout their lifespan by accumulating diverse knowledge and fine-tuning it for future tasks. When presented with a similar goal, neural networks suffer from catastrophic forgetting if data distributions across sequential tasks are not stationary over the course of learning. An effective app... |
Watson_Learning_How_to_MIMIC_Using_Model_Explanations_To_Guide_Deep_WACV_2023_paper | Learning How to MIMIC: Using Model Explanations To Guide Deep Learning Training | [
"Matthew Watson",
"Bashar Awwad Shiekh Hasan",
"Noura Al Moubayed"
] | https://openaccess.thecvf.com/content/WACV2023/html/Watson_Learning_How_to_MIMIC_Using_Model_Explanations_To_Guide_Deep_WACV_2023_paper.html | https://openaccess.thecvf.com/content/WACV2023/papers/Watson_Learning_How_to_MIMIC_Using_Model_Explanations_To_Guide_Deep_WACV_2023_paper.pdf | https://openaccess.thecvf.com/content/WACV2023/supplemental/Watson_Learning_How_to_WACV_2023_supplemental.pdf | null | null | @InProceedings{Watson_2023_WACV,
author = {Watson, Matthew and Hasan, Bashar Awwad Shiekh and Al Moubayed, Noura},
title = {Learning How to MIMIC: Using Model Explanations To Guide Deep Learning Training},
booktitle = {Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (... | Healthcare is seen as one of the most influential applications of Deep Learning (DL). Increasingly, DL models are applied in healthcare settings with seemingly high levels of performance on-par with medical experts. Yet, very few are deployed into real-life scenarios with variable success rate. One of the main reasons ... |
Wang_Learning_by_Hallucinating_Vision-Language_Pre-Training_With_Weak_Supervision_WACV_2023_paper | Learning by Hallucinating: Vision-Language Pre-Training With Weak Supervision | [
"Tzu-Jui Julius Wang",
"Jorma Laaksonen",
"Tomas Langer",
"Heikki Arponen",
"Tom E. Bishop"
] | https://openaccess.thecvf.com/content/WACV2023/html/Wang_Learning_by_Hallucinating_Vision-Language_Pre-Training_With_Weak_Supervision_WACV_2023_paper.html | https://openaccess.thecvf.com/content/WACV2023/papers/Wang_Learning_by_Hallucinating_Vision-Language_Pre-Training_With_Weak_Supervision_WACV_2023_paper.pdf | https://openaccess.thecvf.com/content/WACV2023/supplemental/Wang_Learning_by_Hallucinating_WACV_2023_supplemental.pdf | 2210.13591 | cvf | @InProceedings{Wang_2023_WACV,
author = {Wang, Tzu-Jui Julius and Laaksonen, Jorma and Langer, Tomas and Arponen, Heikki and Bishop, Tom E.},
title = {Learning by Hallucinating: Vision-Language Pre-Training With Weak Supervision},
booktitle = {Proceedings of the IEEE/CVF Winter Conference on Applicat... | Weakly-supervised vision-language (V-L) pre-training (W-VLP) aims at learning cross-modal alignment with little or no paired data, such as aligned images and captions. Recent W-VLP methods, which pair visual features with object tags, help achieve performances comparable with some VLP models trained with aligned pairs ... |
Muller_Self-Supervised_Relative_Pose_With_Homography_Model-Fitting_in_the_Loop_WACV_2023_paper | Self-Supervised Relative Pose With Homography Model-Fitting in the Loop | [
"Bruce R. Muller",
"William A. P. Smith"
] | https://openaccess.thecvf.com/content/WACV2023/html/Muller_Self-Supervised_Relative_Pose_With_Homography_Model-Fitting_in_the_Loop_WACV_2023_paper.html | https://openaccess.thecvf.com/content/WACV2023/papers/Muller_Self-Supervised_Relative_Pose_With_Homography_Model-Fitting_in_the_Loop_WACV_2023_paper.pdf | https://openaccess.thecvf.com/content/WACV2023/supplemental/Muller_Self-Supervised_Relative_Pose_WACV_2023_supplemental.pdf | null | null | @InProceedings{Muller_2023_WACV,
author = {Muller, Bruce R. and Smith, William A. P.},
title = {Self-Supervised Relative Pose With Homography Model-Fitting in the Loop},
booktitle = {Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)},
month = {January},
... | We propose a self-supervised method for relative pose estimation for road scenes. By exploiting the approximate planarity of the local ground plane, we can extract a self-supervision signal via cross-projection between images using a homography derived from estimated ground-relative pose. We augment cross-projected per... |
Robbins_CAST_Conditional_Attribute_Subsampling_Toolkit_for_Fine-Grained_Evaluation_WACV_2023_paper | CAST: Conditional Attribute Subsampling Toolkit for Fine-Grained Evaluation | [
"Wes Robbins",
"Steven Zhou",
"Aman Bhatta",
"Chad Mello",
"Vítor Albiero",
"Kevin W. Bowyer",
"Terrance E. Boult"
] | https://openaccess.thecvf.com/content/WACV2023/html/Robbins_CAST_Conditional_Attribute_Subsampling_Toolkit_for_Fine-Grained_Evaluation_WACV_2023_paper.html | https://openaccess.thecvf.com/content/WACV2023/papers/Robbins_CAST_Conditional_Attribute_Subsampling_Toolkit_for_Fine-Grained_Evaluation_WACV_2023_paper.pdf | https://openaccess.thecvf.com/content/WACV2023/supplemental/Robbins_CAST_Conditional_Attribute_WACV_2023_supplemental.pdf | null | null | @InProceedings{Robbins_2023_WACV,
author = {Robbins, Wes and Zhou, Steven and Bhatta, Aman and Mello, Chad and Albiero, V{\'\i}tor and Bowyer, Kevin W. and Boult, Terrance E.},
title = {CAST: Conditional Attribute Subsampling Toolkit for Fine-Grained Evaluation},
booktitle = {Proceedings of the IEEE/... | Thorough evaluation is critical for developing models that are fair and robust. In this work, we describe the Conditional Attribute Subsampling Toolkit (CAST) for selecting data subsets for fine-grained scientific evaluations. Our toolkit efficiently filters data given an arbitrary number of conditions for metadata att... |
Patel_Seq-UPS_Sequential_Uncertainty-Aware_Pseudo-Label_Selection_for_Semi-Supervised_Text_Recognition_WACV_2023_paper | Seq-UPS: Sequential Uncertainty-Aware Pseudo-Label Selection for Semi-Supervised Text Recognition | [
"Gaurav Patel",
"Jan P. Allebach",
"Qiang Qiu"
] | https://openaccess.thecvf.com/content/WACV2023/html/Patel_Seq-UPS_Sequential_Uncertainty-Aware_Pseudo-Label_Selection_for_Semi-Supervised_Text_Recognition_WACV_2023_paper.html | https://openaccess.thecvf.com/content/WACV2023/papers/Patel_Seq-UPS_Sequential_Uncertainty-Aware_Pseudo-Label_Selection_for_Semi-Supervised_Text_Recognition_WACV_2023_paper.pdf | https://openaccess.thecvf.com/content/WACV2023/supplemental/Patel_Seq-UPS_Sequential_Uncertainty-Aware_WACV_2023_supplemental.pdf | 2209.00641 | title_snapshot | @InProceedings{Patel_2023_WACV,
author = {Patel, Gaurav and Allebach, Jan P. and Qiu, Qiang},
title = {Seq-UPS: Sequential Uncertainty-Aware Pseudo-Label Selection for Semi-Supervised Text Recognition},
booktitle = {Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (WAC... | This paper looks at semi-supervised learning (SSL) for image-based text recognition. One of the most popular SSL approaches is pseudo-labeling (PL). PL approaches assign labels to unlabeled data before re-training the model with a combination of labeled and pseudo-labeled data. However, PL methods are severely degraded... |
Canfes_Text_and_Image_Guided_3D_Avatar_Generation_and_Manipulation_WACV_2023_paper | Text and Image Guided 3D Avatar Generation and Manipulation | [
"Zehranaz Canfes",
"M. Furkan Atasoy",
"Alara Dirik",
"Pinar Yanardag"
] | https://openaccess.thecvf.com/content/WACV2023/html/Canfes_Text_and_Image_Guided_3D_Avatar_Generation_and_Manipulation_WACV_2023_paper.html | https://openaccess.thecvf.com/content/WACV2023/papers/Canfes_Text_and_Image_Guided_3D_Avatar_Generation_and_Manipulation_WACV_2023_paper.pdf | https://openaccess.thecvf.com/content/WACV2023/supplemental/Canfes_Text_and_Image_WACV_2023_supplemental.pdf | 2202.06079 | cvf | @InProceedings{Canfes_2023_WACV,
author = {Canfes, Zehranaz and Atasoy, M. Furkan and Dirik, Alara and Yanardag, Pinar},
title = {Text and Image Guided 3D Avatar Generation and Manipulation},
booktitle = {Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)},
mo... | The manipulation of latent space has recently become an interesting topic in the field of generative models. Recent research shows that latent directions can be used to manipulate images towards certain attributes. However, controlling the generation process of 3D generative models remains a challenge. In this work, we... |
Ma_RAST_Restorable_Arbitrary_Style_Transfer_via_Multi-Restoration_WACV_2023_paper | RAST: Restorable Arbitrary Style Transfer via Multi-Restoration | [
"Yingnan Ma",
"Chenqiu Zhao",
"Xudong Li",
"Anup Basu"
] | https://openaccess.thecvf.com/content/WACV2023/html/Ma_RAST_Restorable_Arbitrary_Style_Transfer_via_Multi-Restoration_WACV_2023_paper.html | https://openaccess.thecvf.com/content/WACV2023/papers/Ma_RAST_Restorable_Arbitrary_Style_Transfer_via_Multi-Restoration_WACV_2023_paper.pdf | null | null | null | @InProceedings{Ma_2023_WACV,
author = {Ma, Yingnan and Zhao, Chenqiu and Li, Xudong and Basu, Anup},
title = {RAST: Restorable Arbitrary Style Transfer via Multi-Restoration},
booktitle = {Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)},
month = {Janua... | Arbitrary style transfer aims at reproducing the target image with provided artistic or photo-realistic styles. Even though existing approaches can successfully transfer style information, arbitrary style transfer still faces many challenges, such as the content leak issue. To be specific, the embedding of artistic sty... |
Iwaguchi_Surface_Normal_Estimation_From_Optimized_and_Distributed_Light_Sources_Using_WACV_2023_paper | Surface Normal Estimation From Optimized and Distributed Light Sources Using DNN-Based Photometric Stereo | [
"Takafumi Iwaguchi",
"Hiroshi Kawasaki"
] | https://openaccess.thecvf.com/content/WACV2023/html/Iwaguchi_Surface_Normal_Estimation_From_Optimized_and_Distributed_Light_Sources_Using_WACV_2023_paper.html | https://openaccess.thecvf.com/content/WACV2023/papers/Iwaguchi_Surface_Normal_Estimation_From_Optimized_and_Distributed_Light_Sources_Using_WACV_2023_paper.pdf | null | null | null | @InProceedings{Iwaguchi_2023_WACV,
author = {Iwaguchi, Takafumi and Kawasaki, Hiroshi},
title = {Surface Normal Estimation From Optimized and Distributed Light Sources Using DNN-Based Photometric Stereo},
booktitle = {Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (W... | Photometric stereo (PS) is a major technique to recover surface normal for each pixel. However, since it assumes Lambertian surface and directional light to estimate the value, a large number of images are usually required to avoid the effects of outliers and noise. In this paper, we propose a technique to reduce the n... |
Specker_UPAR_Unified_Pedestrian_Attribute_Recognition_and_Person_Retrieval_WACV_2023_paper | UPAR: Unified Pedestrian Attribute Recognition and Person Retrieval | [
"Andreas Specker",
"Mickael Cormier",
"Jürgen Beyerer"
] | https://openaccess.thecvf.com/content/WACV2023/html/Specker_UPAR_Unified_Pedestrian_Attribute_Recognition_and_Person_Retrieval_WACV_2023_paper.html | https://openaccess.thecvf.com/content/WACV2023/papers/Specker_UPAR_Unified_Pedestrian_Attribute_Recognition_and_Person_Retrieval_WACV_2023_paper.pdf | https://openaccess.thecvf.com/content/WACV2023/supplemental/Specker_UPAR_Unified_Pedestrian_WACV_2023_supplemental.pdf | 2209.02522 | cvf | @InProceedings{Specker_2023_WACV,
author = {Specker, Andreas and Cormier, Mickael and Beyerer, J\"urgen},
title = {UPAR: Unified Pedestrian Attribute Recognition and Person Retrieval},
booktitle = {Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)},
month ... | Recognizing soft-biometric pedestrian attributes is essential in video surveillance and fashion retrieval. Recent works show promising results on single datasets. Nevertheless, the generalization ability of these methods under different attribute distributions, viewpoints, varying illumination, and low resolutions rema... |
Garg_Instance-Dependent_Noisy_Label_Learning_via_Graphical_Modelling_WACV_2023_paper | Instance-Dependent Noisy Label Learning via Graphical Modelling | [
"Arpit Garg",
"Cuong Nguyen",
"Rafael Felix",
"Thanh-Toan Do",
"Gustavo Carneiro"
] | https://openaccess.thecvf.com/content/WACV2023/html/Garg_Instance-Dependent_Noisy_Label_Learning_via_Graphical_Modelling_WACV_2023_paper.html | https://openaccess.thecvf.com/content/WACV2023/papers/Garg_Instance-Dependent_Noisy_Label_Learning_via_Graphical_Modelling_WACV_2023_paper.pdf | https://openaccess.thecvf.com/content/WACV2023/supplemental/Garg_Instance-Dependent_Noisy_Label_WACV_2023_supplemental.pdf | 2209.00906 | cvf | @InProceedings{Garg_2023_WACV,
author = {Garg, Arpit and Nguyen, Cuong and Felix, Rafael and Do, Thanh-Toan and Carneiro, Gustavo},
title = {Instance-Dependent Noisy Label Learning via Graphical Modelling},
booktitle = {Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision ... | Noisy labels are unavoidable yet troublesome in the ecosystem of deep learning because models can easily overfit them. There are many types of label noise, such as symmetric, asymmetric and instance-dependent noise (IDN), with IDN being the only type that depends on image information. Such dependence on image informati... |
Brummer_On_the_Importance_of_Denoising_When_Learning_To_Compress_Images_WACV_2023_paper | On the Importance of Denoising When Learning To Compress Images | [
"Benoit Brummer",
"Christophe De Vleeschouwer"
] | https://openaccess.thecvf.com/content/WACV2023/html/Brummer_On_the_Importance_of_Denoising_When_Learning_To_Compress_Images_WACV_2023_paper.html | https://openaccess.thecvf.com/content/WACV2023/papers/Brummer_On_the_Importance_of_Denoising_When_Learning_To_Compress_Images_WACV_2023_paper.pdf | null | 2307.06233 | title_snapshot | @InProceedings{Brummer_2023_WACV,
author = {Brummer, Benoit and De Vleeschouwer, Christophe},
title = {On the Importance of Denoising When Learning To Compress Images},
booktitle = {Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)},
month = {January},
... | Image noise is ubiquitous in photography. However, image noise is not compressible nor desirable, thus attempting to convey the noise in compressed image bitstreams yields sub-par results in both rate and distortion. We propose to explicitly learn the image denoising task when training the codec. Therefore, we leverage... |
Dubey_AdaNorm_Adaptive_Gradient_Norm_Correction_Based_Optimizer_for_CNNs_WACV_2023_paper | AdaNorm: Adaptive Gradient Norm Correction Based Optimizer for CNNs | [
"Shiv Ram Dubey",
"Satish Kumar Singh",
"Bidyut Baran Chaudhuri"
] | https://openaccess.thecvf.com/content/WACV2023/html/Dubey_AdaNorm_Adaptive_Gradient_Norm_Correction_Based_Optimizer_for_CNNs_WACV_2023_paper.html | https://openaccess.thecvf.com/content/WACV2023/papers/Dubey_AdaNorm_Adaptive_Gradient_Norm_Correction_Based_Optimizer_for_CNNs_WACV_2023_paper.pdf | https://openaccess.thecvf.com/content/WACV2023/supplemental/Dubey_AdaNorm_Adaptive_Gradient_WACV_2023_supplemental.pdf | 2210.06364 | cvf | @InProceedings{Dubey_2023_WACV,
author = {Dubey, Shiv Ram and Singh, Satish Kumar and Chaudhuri, Bidyut Baran},
title = {AdaNorm: Adaptive Gradient Norm Correction Based Optimizer for CNNs},
booktitle = {Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)},
mon... | The stochastic gradient descent (SGD) optimizers are generally used to train the convolutional neural networks (CNNs). In recent years, several adaptive momentum based SGD optimizers have been introduced, such as Adam, diffGrad, Radam and AdaBelief. However, the existing SGD optimizers do not exploit the gradient norm ... |
Kulkarni_Aerial_Image_Dehazing_With_Attentive_Deformable_Transformers_WACV_2023_paper | Aerial Image Dehazing With Attentive Deformable Transformers | [
"Ashutosh Kulkarni",
"Subrahmanyam Murala"
] | https://openaccess.thecvf.com/content/WACV2023/html/Kulkarni_Aerial_Image_Dehazing_With_Attentive_Deformable_Transformers_WACV_2023_paper.html | https://openaccess.thecvf.com/content/WACV2023/papers/Kulkarni_Aerial_Image_Dehazing_With_Attentive_Deformable_Transformers_WACV_2023_paper.pdf | https://openaccess.thecvf.com/content/WACV2023/supplemental/Kulkarni_Aerial_Image_Dehazing_WACV_2023_supplemental.pdf | null | null | @InProceedings{Kulkarni_2023_WACV,
author = {Kulkarni, Ashutosh and Murala, Subrahmanyam},
title = {Aerial Image Dehazing With Attentive Deformable Transformers},
booktitle = {Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)},
month = {January},
year... | Aerial imagery is widely utilized in visual data dependent applications such as military surveillance, earthquake assessment, etc. For these applications, minute texture in the aerial image are essential as any disturbance can cause inaccurate prediction. However, atmospheric haze severely reduces the visibility of the... |
Luzi_Evaluating_Generative_Networks_Using_Gaussian_Mixtures_of_Image_Features_WACV_2023_paper | Evaluating Generative Networks Using Gaussian Mixtures of Image Features | [
"Lorenzo Luzi",
"Carlos Ortiz Marrero",
"Nile Wynar",
"Richard G. Baraniuk",
"Michael J. Henry"
] | https://openaccess.thecvf.com/content/WACV2023/html/Luzi_Evaluating_Generative_Networks_Using_Gaussian_Mixtures_of_Image_Features_WACV_2023_paper.html | https://openaccess.thecvf.com/content/WACV2023/papers/Luzi_Evaluating_Generative_Networks_Using_Gaussian_Mixtures_of_Image_Features_WACV_2023_paper.pdf | https://openaccess.thecvf.com/content/WACV2023/supplemental/Luzi_Evaluating_Generative_Networks_WACV_2023_supplemental.pdf | 2110.05240 | cvf | @InProceedings{Luzi_2023_WACV,
author = {Luzi, Lorenzo and Marrero, Carlos Ortiz and Wynar, Nile and Baraniuk, Richard G. and Henry, Michael J.},
title = {Evaluating Generative Networks Using Gaussian Mixtures of Image Features},
booktitle = {Proceedings of the IEEE/CVF Winter Conference on Applicati... | We develop a measure for evaluating the performance of generative networks given two sets of images. A popular performance measure currently used to do this is the Frechet Inception Distance (FID). FID assumes that images featurized using the penultimate layer of Inception-v3 follow a Gaussian distribution, an assumpti... |
Conti_Sparsity_Agnostic_Depth_Completion_WACV_2023_paper | Sparsity Agnostic Depth Completion | [
"Andrea Conti",
"Matteo Poggi",
"Stefano Mattoccia"
] | https://openaccess.thecvf.com/content/WACV2023/html/Conti_Sparsity_Agnostic_Depth_Completion_WACV_2023_paper.html | https://openaccess.thecvf.com/content/WACV2023/papers/Conti_Sparsity_Agnostic_Depth_Completion_WACV_2023_paper.pdf | https://openaccess.thecvf.com/content/WACV2023/supplemental/Conti_Sparsity_Agnostic_Depth_WACV_2023_supplemental.zip | 2212.00790 | cvf | @InProceedings{Conti_2023_WACV,
author = {Conti, Andrea and Poggi, Matteo and Mattoccia, Stefano},
title = {Sparsity Agnostic Depth Completion},
booktitle = {Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)},
month = {January},
year = {2023},
... | We present a novel depth completion approach agnostic to the sparsity of depth points, that is very likely to vary in many practical applications. State-of-the-art approaches yield accurate results only when processing a specific density and distribution of input points, i.e. the one observed during training, narrowing... |
Bose_MovieCLIP_Visual_Scene_Recognition_in_Movies_WACV_2023_paper | MovieCLIP: Visual Scene Recognition in Movies | [
"Digbalay Bose",
"Rajat Hebbar",
"Krishna Somandepalli",
"Haoyang Zhang",
"Yin Cui",
"Kree Cole-McLaughlin",
"Huisheng Wang",
"Shrikanth Narayanan"
] | https://openaccess.thecvf.com/content/WACV2023/html/Bose_MovieCLIP_Visual_Scene_Recognition_in_Movies_WACV_2023_paper.html | https://openaccess.thecvf.com/content/WACV2023/papers/Bose_MovieCLIP_Visual_Scene_Recognition_in_Movies_WACV_2023_paper.pdf | https://openaccess.thecvf.com/content/WACV2023/supplemental/Bose_MovieCLIP_Visual_Scene_WACV_2023_supplemental.pdf | 2210.11065 | cvf | @InProceedings{Bose_2023_WACV,
author = {Bose, Digbalay and Hebbar, Rajat and Somandepalli, Krishna and Zhang, Haoyang and Cui, Yin and Cole-McLaughlin, Kree and Wang, Huisheng and Narayanan, Shrikanth},
title = {MovieCLIP: Visual Scene Recognition in Movies},
booktitle = {Proceedings of the IEEE/CVF... | Longform media such as movies have complex narrative structures, with events spanning a rich variety of ambient visual scenes. Domain-specific challenges associated with visual scenes in movies include transitions, person coverage, and a wide array of real-life and fictional scenarios. Existing visual scene datasets in... |
Peng_Dynamic_Re-Weighting_for_Long-Tailed_Semi-Supervised_Learning_WACV_2023_paper | Dynamic Re-Weighting for Long-Tailed Semi-Supervised Learning | [
"Hanyu Peng",
"Weiguo Pian",
"Mingming Sun",
"Ping Li"
] | https://openaccess.thecvf.com/content/WACV2023/html/Peng_Dynamic_Re-Weighting_for_Long-Tailed_Semi-Supervised_Learning_WACV_2023_paper.html | https://openaccess.thecvf.com/content/WACV2023/papers/Peng_Dynamic_Re-Weighting_for_Long-Tailed_Semi-Supervised_Learning_WACV_2023_paper.pdf | https://openaccess.thecvf.com/content/WACV2023/supplemental/Peng_Dynamic_Re-Weighting_for_WACV_2023_supplemental.pdf | null | null | @InProceedings{Peng_2023_WACV,
author = {Peng, Hanyu and Pian, Weiguo and Sun, Mingming and Li, Ping},
title = {Dynamic Re-Weighting for Long-Tailed Semi-Supervised Learning},
booktitle = {Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)},
month = {Janua... | The high demand for labeled data that characterizes deep learning is very labor-intensive. Semi-supervised Learning (SSL), acting as one of the breakthroughs, allows for the avoidance of this labeling loss thanks to its small amount of labeled data, alongside extracting information from a large amount of unlabeled data... |
Tripathi_Grounding_Scene_Graphs_on_Natural_Images_via_Visio-Lingual_Message_Passing_WACV_2023_paper | Grounding Scene Graphs on Natural Images via Visio-Lingual Message Passing | [
"Aditay Tripathi",
"Anand Mishra",
"Anirban Chakraborty"
] | https://openaccess.thecvf.com/content/WACV2023/html/Tripathi_Grounding_Scene_Graphs_on_Natural_Images_via_Visio-Lingual_Message_Passing_WACV_2023_paper.html | https://openaccess.thecvf.com/content/WACV2023/papers/Tripathi_Grounding_Scene_Graphs_on_Natural_Images_via_Visio-Lingual_Message_Passing_WACV_2023_paper.pdf | https://openaccess.thecvf.com/content/WACV2023/supplemental/Tripathi_Grounding_Scene_Graphs_WACV_2023_supplemental.pdf | 2211.01969 | cvf | @InProceedings{Tripathi_2023_WACV,
author = {Tripathi, Aditay and Mishra, Anand and Chakraborty, Anirban},
title = {Grounding Scene Graphs on Natural Images via Visio-Lingual Message Passing},
booktitle = {Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)},
m... | This paper presents a framework for jointly grounding objects that follow certain semantic relationship constraints given in a scene graph. A typical natural scene contains several objects, often exhibiting visual relationships of varied complexities between them. These inter-object relationships provide strong context... |
Lee_Improving_Multi-Fidelity_Optimization_With_a_Recurring_Learning_Rate_for_Hyperparameter_WACV_2023_paper | Improving Multi-Fidelity Optimization With a Recurring Learning Rate for Hyperparameter Tuning | [
"HyunJae Lee",
"Gihyeon Lee",
"Junhwan Kim",
"Sungjun Cho",
"Dohyun Kim",
"Donggeun Yoo"
] | https://openaccess.thecvf.com/content/WACV2023/html/Lee_Improving_Multi-Fidelity_Optimization_With_a_Recurring_Learning_Rate_for_Hyperparameter_WACV_2023_paper.html | https://openaccess.thecvf.com/content/WACV2023/papers/Lee_Improving_Multi-Fidelity_Optimization_With_a_Recurring_Learning_Rate_for_Hyperparameter_WACV_2023_paper.pdf | null | 2209.12499 | cvf | @InProceedings{Lee_2023_WACV,
author = {Lee, HyunJae and Lee, Gihyeon and Kim, Junhwan and Cho, Sungjun and Kim, Dohyun and Yoo, Donggeun},
title = {Improving Multi-Fidelity Optimization With a Recurring Learning Rate for Hyperparameter Tuning},
booktitle = {Proceedings of the IEEE/CVF Winter Confere... | Despite the evolution of Convolutional Neural Networks (CNNs), their performance is surprisingly dependent on the choice of hyperparameters. However, it remains challenging to efficiently explore large hyperparameter search space due to the long training times of modern CNNs. Multi-fidelity optimization enables the exp... |
Liu_TI2Net_Temporal_Identity_Inconsistency_Network_for_Deepfake_Detection_WACV_2023_paper | TI2Net: Temporal Identity Inconsistency Network for Deepfake Detection | [
"Baoping Liu",
"Bo Liu",
"Ming Ding",
"Tianqing Zhu",
"Xin Yu"
] | https://openaccess.thecvf.com/content/WACV2023/html/Liu_TI2Net_Temporal_Identity_Inconsistency_Network_for_Deepfake_Detection_WACV_2023_paper.html | https://openaccess.thecvf.com/content/WACV2023/papers/Liu_TI2Net_Temporal_Identity_Inconsistency_Network_for_Deepfake_Detection_WACV_2023_paper.pdf | https://openaccess.thecvf.com/content/WACV2023/supplemental/Liu_TI2Net_Temporal_Identity_WACV_2023_supplemental.pdf | null | null | @InProceedings{Liu_2023_WACV,
author = {Liu, Baoping and Liu, Bo and Ding, Ming and Zhu, Tianqing and Yu, Xin},
title = {TI2Net: Temporal Identity Inconsistency Network for Deepfake Detection},
booktitle = {Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)},
... | In this paper, we propose a Temporal Identity Inconsistency Network (TI2Net), a Deepfake detector that focuses on temporal identity inconsistency. Specifically, TI2Net recognizes fake videos by capturing the dissimilarities of human faces among video frames of the same identity. Therefore, TI2Net is a reference-agnosti... |
Stegmuller_ScoreNet_Learning_Non-Uniform_Attention_and_Augmentation_for_Transformer-Based_Histopathological_Image_WACV_2023_paper | ScoreNet: Learning Non-Uniform Attention and Augmentation for Transformer-Based Histopathological Image Classification | [
"Thomas Stegmüller",
"Behzad Bozorgtabar",
"Antoine Spahr",
"Jean-Philippe Thiran"
] | https://openaccess.thecvf.com/content/WACV2023/html/Stegmuller_ScoreNet_Learning_Non-Uniform_Attention_and_Augmentation_for_Transformer-Based_Histopathological_Image_WACV_2023_paper.html | https://openaccess.thecvf.com/content/WACV2023/papers/Stegmuller_ScoreNet_Learning_Non-Uniform_Attention_and_Augmentation_for_Transformer-Based_Histopathological_Image_WACV_2023_paper.pdf | https://openaccess.thecvf.com/content/WACV2023/supplemental/Stegmuller_ScoreNet_Learning_Non-Uniform_WACV_2023_supplemental.pdf | 2202.07570 | title_snapshot | @InProceedings{Stegmuller_2023_WACV,
author = {Stegm\"uller, Thomas and Bozorgtabar, Behzad and Spahr, Antoine and Thiran, Jean-Philippe},
title = {ScoreNet: Learning Non-Uniform Attention and Augmentation for Transformer-Based Histopathological Image Classification},
booktitle = {Proceedings of the ... | Progress in digital pathology is hindered by high-resolution images and the prohibitive cost of exhaustive localized annotations. The commonly used paradigm to categorize pathology images is patch-based processing, which often incorporates multiple instance learning MIL to aggregate local patch-level representations yi... |
Zhang_Cross-View_Image_Sequence_Geo-Localization_WACV_2023_paper | Cross-View Image Sequence Geo-Localization | [
"Xiaohan Zhang",
"Waqas Sultani",
"Safwan Wshah"
] | https://openaccess.thecvf.com/content/WACV2023/html/Zhang_Cross-View_Image_Sequence_Geo-Localization_WACV_2023_paper.html | https://openaccess.thecvf.com/content/WACV2023/papers/Zhang_Cross-View_Image_Sequence_Geo-Localization_WACV_2023_paper.pdf | https://openaccess.thecvf.com/content/WACV2023/supplemental/Zhang_Cross-View_Image_Sequence_WACV_2023_supplemental.pdf | 2210.14295 | cvf | @InProceedings{Zhang_2023_WACV,
author = {Zhang, Xiaohan and Sultani, Waqas and Wshah, Safwan},
title = {Cross-View Image Sequence Geo-Localization},
booktitle = {Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)},
month = {January},
year = {2023... | Cross-view geo-localization aims to estimate the GPS location of a query ground-view image by matching it to images from a reference database of geo-tagged aerial images. To address this challenging problem, recent approaches use panoramic ground-view images to increase the range of visibility. Although appealing, pano... |
Bai_CoKe_Contrastive_Learning_for_Robust_Keypoint_Detection_WACV_2023_paper | CoKe: Contrastive Learning for Robust Keypoint Detection | [
"Yutong Bai",
"Angtian Wang",
"Adam Kortylewski",
"Alan Yuille"
] | https://openaccess.thecvf.com/content/WACV2023/html/Bai_CoKe_Contrastive_Learning_for_Robust_Keypoint_Detection_WACV_2023_paper.html | https://openaccess.thecvf.com/content/WACV2023/papers/Bai_CoKe_Contrastive_Learning_for_Robust_Keypoint_Detection_WACV_2023_paper.pdf | null | null | null | @InProceedings{Bai_2023_WACV,
author = {Bai, Yutong and Wang, Angtian and Kortylewski, Adam and Yuille, Alan},
title = {CoKe: Contrastive Learning for Robust Keypoint Detection},
booktitle = {Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)},
month = {Ja... | In this paper, we introduce a contrastive learning framework for keypoint detection (CoKe). Keypoint detection differs from other visual tasks where contrastive learning has been applied because the input is a set of images in which multiple keypoints are annotated. This requires the contrastive learning to be extended... |
Athar_BURST_A_Benchmark_for_Unifying_Object_Recognition_Segmentation_and_Tracking_WACV_2023_paper | BURST: A Benchmark for Unifying Object Recognition, Segmentation and Tracking in Video | [
"Ali Athar",
"Jonathon Luiten",
"Paul Voigtlaender",
"Tarasha Khurana",
"Achal Dave",
"Bastian Leibe",
"Deva Ramanan"
] | https://openaccess.thecvf.com/content/WACV2023/html/Athar_BURST_A_Benchmark_for_Unifying_Object_Recognition_Segmentation_and_Tracking_WACV_2023_paper.html | https://openaccess.thecvf.com/content/WACV2023/papers/Athar_BURST_A_Benchmark_for_Unifying_Object_Recognition_Segmentation_and_Tracking_WACV_2023_paper.pdf | https://openaccess.thecvf.com/content/WACV2023/supplemental/Athar_BURST_A_Benchmark_WACV_2023_supplemental.zip | 2209.12118 | cvf | @InProceedings{Athar_2023_WACV,
author = {Athar, Ali and Luiten, Jonathon and Voigtlaender, Paul and Khurana, Tarasha and Dave, Achal and Leibe, Bastian and Ramanan, Deva},
title = {BURST: A Benchmark for Unifying Object Recognition, Segmentation and Tracking in Video},
booktitle = {Proceedings of th... | Multiple existing benchmarks involve tracking and segmenting objects in video e.g., Video Object Segmentation (VOS) and Multi-Object Tracking and Segmentation (MOTS), but there is little interaction between them due to the use of disparate benchmark datasets and metrics (e.g. \JnF, mAP, sMOTSA). As a result, published ... |
Pham_Collaborative_Multi-Teacher_Knowledge_Distillation_for_Learning_Low_Bit-Width_Deep_Neural_WACV_2023_paper | Collaborative Multi-Teacher Knowledge Distillation for Learning Low Bit-Width Deep Neural Networks | [
"Cuong Pham",
"Tuan Hoang",
"Thanh-Toan Do"
] | https://openaccess.thecvf.com/content/WACV2023/html/Pham_Collaborative_Multi-Teacher_Knowledge_Distillation_for_Learning_Low_Bit-Width_Deep_Neural_WACV_2023_paper.html | https://openaccess.thecvf.com/content/WACV2023/papers/Pham_Collaborative_Multi-Teacher_Knowledge_Distillation_for_Learning_Low_Bit-Width_Deep_Neural_WACV_2023_paper.pdf | null | 2210.16103 | cvf | @InProceedings{Pham_2023_WACV,
author = {Pham, Cuong and Hoang, Tuan and Do, Thanh-Toan},
title = {Collaborative Multi-Teacher Knowledge Distillation for Learning Low Bit-Width Deep Neural Networks},
booktitle = {Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)}... | Knowledge distillation which learns a lightweight student model by distilling knowledge from a cumbersome teacher model is an attractive approach for learning compact deep neural networks (DNNs). Recent works further improve student network performance by leveraging multiple teacher networks. However, most of the exist... |
Dawoud_Knowing_What_To_Label_for_Few_Shot_Microscopy_Image_Cell_WACV_2023_paper | Knowing What To Label for Few Shot Microscopy Image Cell Segmentation | [
"Youssef Dawoud",
"Arij Bouazizi",
"Katharina Ernst",
"Gustavo Carneiro",
"Vasileios Belagiannis"
] | https://openaccess.thecvf.com/content/WACV2023/html/Dawoud_Knowing_What_To_Label_for_Few_Shot_Microscopy_Image_Cell_WACV_2023_paper.html | https://openaccess.thecvf.com/content/WACV2023/papers/Dawoud_Knowing_What_To_Label_for_Few_Shot_Microscopy_Image_Cell_WACV_2023_paper.pdf | https://openaccess.thecvf.com/content/WACV2023/supplemental/Dawoud_Knowing_What_To_WACV_2023_supplemental.pdf | 2211.10244 | cvf | @InProceedings{Dawoud_2023_WACV,
author = {Dawoud, Youssef and Bouazizi, Arij and Ernst, Katharina and Carneiro, Gustavo and Belagiannis, Vasileios},
title = {Knowing What To Label for Few Shot Microscopy Image Cell Segmentation},
booktitle = {Proceedings of the IEEE/CVF Winter Conference on Applicat... | In microscopy image cell segmentation, it is common to train a deep neural network on source data, containing different types of microscopy images, and then fine-tune it using a support set comprising a few randomly selected and annotated training target images. In this paper, we argue that the random selection of unla... |
Pan_SSFE-Net_Self-Supervised_Feature_Enhancement_for_Ultra-Fine-Grained_Few-Shot_Class_Incremental_Learning_WACV_2023_paper | SSFE-Net: Self-Supervised Feature Enhancement for Ultra-Fine-Grained Few-Shot Class Incremental Learning | [
"Zicheng Pan",
"Xiaohan Yu",
"Miaohua Zhang",
"Yongsheng Gao"
] | https://openaccess.thecvf.com/content/WACV2023/html/Pan_SSFE-Net_Self-Supervised_Feature_Enhancement_for_Ultra-Fine-Grained_Few-Shot_Class_Incremental_Learning_WACV_2023_paper.html | https://openaccess.thecvf.com/content/WACV2023/papers/Pan_SSFE-Net_Self-Supervised_Feature_Enhancement_for_Ultra-Fine-Grained_Few-Shot_Class_Incremental_Learning_WACV_2023_paper.pdf | https://openaccess.thecvf.com/content/WACV2023/supplemental/Pan_SSFE-Net_Self-Supervised_Feature_WACV_2023_supplemental.pdf | null | null | @InProceedings{Pan_2023_WACV,
author = {Pan, Zicheng and Yu, Xiaohan and Zhang, Miaohua and Gao, Yongsheng},
title = {SSFE-Net: Self-Supervised Feature Enhancement for Ultra-Fine-Grained Few-Shot Class Incremental Learning},
booktitle = {Proceedings of the IEEE/CVF Winter Conference on Applications o... | Ultra-Fine-Grained Visual Categorization (ultra-FGVC) has become a popular problem due to its great real-world potential for classifying the same or closely related species with very similar layouts. However, there present many challenges for the existing ultra-FGVC methods, firstly there are always not enough samples ... |
Davila_MEVID_Multi-View_Extended_Videos_With_Identities_for_Video_Person_Re-Identification_WACV_2023_paper | MEVID: Multi-View Extended Videos With Identities for Video Person Re-Identification | [
"Daniel Davila",
"Dawei Du",
"Bryon Lewis",
"Christopher Funk",
"Joseph Van Pelt",
"Roderic Collins",
"Kellie Corona",
"Matt Brown",
"Scott McCloskey",
"Anthony Hoogs",
"Brian Clipp"
] | https://openaccess.thecvf.com/content/WACV2023/html/Davila_MEVID_Multi-View_Extended_Videos_With_Identities_for_Video_Person_Re-Identification_WACV_2023_paper.html | https://openaccess.thecvf.com/content/WACV2023/papers/Davila_MEVID_Multi-View_Extended_Videos_With_Identities_for_Video_Person_Re-Identification_WACV_2023_paper.pdf | null | 2211.04656 | cvf | @InProceedings{Davila_2023_WACV,
author = {Davila, Daniel and Du, Dawei and Lewis, Bryon and Funk, Christopher and Van Pelt, Joseph and Collins, Roderic and Corona, Kellie and Brown, Matt and McCloskey, Scott and Hoogs, Anthony and Clipp, Brian},
title = {MEVID: Multi-View Extended Videos With Identities... | In this paper, we present the Multi-view Extended Videos with Identities (MEVID) dataset for large-scale, video person re-identification (ReID) in the wild. To our knowledge, MEVID represents the most-varied video person ReID dataset, spanning an extensive indoor and outdoor environment across nine unique dates in a 73... |
Burchi_Audio-Visual_Efficient_Conformer_for_Robust_Speech_Recognition_WACV_2023_paper | Audio-Visual Efficient Conformer for Robust Speech Recognition | [
"Maxime Burchi",
"Radu Timofte"
] | https://openaccess.thecvf.com/content/WACV2023/html/Burchi_Audio-Visual_Efficient_Conformer_for_Robust_Speech_Recognition_WACV_2023_paper.html | https://openaccess.thecvf.com/content/WACV2023/papers/Burchi_Audio-Visual_Efficient_Conformer_for_Robust_Speech_Recognition_WACV_2023_paper.pdf | null | 2301.01456 | title_snapshot | @InProceedings{Burchi_2023_WACV,
author = {Burchi, Maxime and Timofte, Radu},
title = {Audio-Visual Efficient Conformer for Robust Speech Recognition},
booktitle = {Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)},
month = {January},
year = {20... | End-to-end Automatic Speech Recognition (ASR) systems based on neural networks have seen large improvements in recent years. The availability of large scale hand-labeled datasets and sufficient computing resources made it possible to train powerful deep neural networks, reaching very low Word Error Rate (WER) on academ... |
Bae_DigiFace-1M_1_Million_Digital_Face_Images_for_Face_Recognition_WACV_2023_paper | DigiFace-1M: 1 Million Digital Face Images for Face Recognition | [
"Gwangbin Bae",
"Martin de La Gorce",
"Tadas Baltrušaitis",
"Charlie Hewitt",
"Dong Chen",
"Julien Valentin",
"Roberto Cipolla",
"Jingjing Shen"
] | https://openaccess.thecvf.com/content/WACV2023/html/Bae_DigiFace-1M_1_Million_Digital_Face_Images_for_Face_Recognition_WACV_2023_paper.html | https://openaccess.thecvf.com/content/WACV2023/papers/Bae_DigiFace-1M_1_Million_Digital_Face_Images_for_Face_Recognition_WACV_2023_paper.pdf | null | 2210.02579 | title_snapshot | @InProceedings{Bae_2023_WACV,
author = {Bae, Gwangbin and de La Gorce, Martin and Baltru\v{s}aitis, Tadas and Hewitt, Charlie and Chen, Dong and Valentin, Julien and Cipolla, Roberto and Shen, Jingjing},
title = {DigiFace-1M: 1 Million Digital Face Images for Face Recognition},
booktitle = {Proceedin... | State-of-the-art face recognition models show impressive accuracy, achieving over 99.8% on Labeled Faces in the Wild (LFW) dataset. Such models are trained on large-scale datasets that contain millions of real human face images collected from the internet. Web-crawled face images are severely biased (in terms of race, ... |
Lan_Couplformer_Rethinking_Vision_Transformer_With_Coupling_Attention_WACV_2023_paper | Couplformer: Rethinking Vision Transformer With Coupling Attention | [
"Hai Lan",
"Xihao Wang",
"Hao Shen",
"Peidong Liang",
"Xian Wei"
] | https://openaccess.thecvf.com/content/WACV2023/html/Lan_Couplformer_Rethinking_Vision_Transformer_With_Coupling_Attention_WACV_2023_paper.html | https://openaccess.thecvf.com/content/WACV2023/papers/Lan_Couplformer_Rethinking_Vision_Transformer_With_Coupling_Attention_WACV_2023_paper.pdf | https://openaccess.thecvf.com/content/WACV2023/supplemental/Lan_Couplformer_Rethinking_Vision_WACV_2023_supplemental.pdf | 2112.05425 | title_judge | @InProceedings{Lan_2023_WACV,
author = {Lan, Hai and Wang, Xihao and Shen, Hao and Liang, Peidong and Wei, Xian},
title = {Couplformer: Rethinking Vision Transformer With Coupling Attention},
booktitle = {Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)},
mo... | With the development of the self-attention mechanism, the Transformer model has demonstrated its outstanding performance in the computer vision domain. However, the massive computation brought from the full attention mechanism became a heavy burden for memory consumption. Sequentially, the limitation of memory consumpt... |
Wyzykowski_Synthetic_Latent_Fingerprint_Generator_WACV_2023_paper | Synthetic Latent Fingerprint Generator | [
"André Brasil Vieira Wyzykowski",
"Anil K. Jain"
] | https://openaccess.thecvf.com/content/WACV2023/html/Wyzykowski_Synthetic_Latent_Fingerprint_Generator_WACV_2023_paper.html | https://openaccess.thecvf.com/content/WACV2023/papers/Wyzykowski_Synthetic_Latent_Fingerprint_Generator_WACV_2023_paper.pdf | null | 2208.13811 | cvf | @InProceedings{Wyzykowski_2023_WACV,
author = {Wyzykowski, Andr\'e Brasil Vieira and Jain, Anil K.},
title = {Synthetic Latent Fingerprint Generator},
booktitle = {Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)},
month = {January},
year = {202... | Given a full fingerprint image (rolled or slap), we present CycleGAN models to generate multiple latent impressions of the same identity as the full print. Our models can control the degree of distortion, noise, blurriness and occlusion in the generated latent print images to obtain Good, Bad and Ugly latent image cate... |
Chen_Accumulated_Trivial_Attention_Matters_in_Vision_Transformers_on_Small_Datasets_WACV_2023_paper | Accumulated Trivial Attention Matters in Vision Transformers on Small Datasets | [
"Xiangyu Chen",
"Qinghao Hu",
"Kaidong Li",
"Cuncong Zhong",
"Guanghui Wang"
] | https://openaccess.thecvf.com/content/WACV2023/html/Chen_Accumulated_Trivial_Attention_Matters_in_Vision_Transformers_on_Small_Datasets_WACV_2023_paper.html | https://openaccess.thecvf.com/content/WACV2023/papers/Chen_Accumulated_Trivial_Attention_Matters_in_Vision_Transformers_on_Small_Datasets_WACV_2023_paper.pdf | null | 2210.12333 | cvf | @InProceedings{Chen_2023_WACV,
author = {Chen, Xiangyu and Hu, Qinghao and Li, Kaidong and Zhong, Cuncong and Wang, Guanghui},
title = {Accumulated Trivial Attention Matters in Vision Transformers on Small Datasets},
booktitle = {Proceedings of the IEEE/CVF Winter Conference on Applications of Comput... | Vision Transformers has demonstrated competitive performance on computer vision tasks benefiting from their ability to capture long-range dependencies with multi-head self-attention modules and multi-layer perceptron. However, calculating global attention brings another disadvantage compared with convolutional neural n... |
Ge_Cross-Modal_Semantic_Enhanced_Interaction_for_Image-Sentence_Retrieval_WACV_2023_paper | Cross-Modal Semantic Enhanced Interaction for Image-Sentence Retrieval | [
"Xuri Ge",
"Fuhai Chen",
"Songpei Xu",
"Fuxiang Tao",
"Joemon M. Jose"
] | https://openaccess.thecvf.com/content/WACV2023/html/Ge_Cross-Modal_Semantic_Enhanced_Interaction_for_Image-Sentence_Retrieval_WACV_2023_paper.html | https://openaccess.thecvf.com/content/WACV2023/papers/Ge_Cross-Modal_Semantic_Enhanced_Interaction_for_Image-Sentence_Retrieval_WACV_2023_paper.pdf | null | 2210.08908 | cvf | @InProceedings{Ge_2023_WACV,
author = {Ge, Xuri and Chen, Fuhai and Xu, Songpei and Tao, Fuxiang and Jose, Joemon M.},
title = {Cross-Modal Semantic Enhanced Interaction for Image-Sentence Retrieval},
booktitle = {Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)... | Image-sentence retrieval has attracted extensive research attention in multimedia and computer vision due to its promising application. The key issue lies in jointly learning the visual and textual representation to accurately estimate their similarity. To this end, the mainstream schema adopts an object-word based att... |
Guirguis_Towards_Discriminative_and_Transferable_One-Stage_Few-Shot_Object_Detectors_WACV_2023_paper | Towards Discriminative and Transferable One-Stage Few-Shot Object Detectors | [
"Karim Guirguis",
"Mohamed Abdelsamad",
"George Eskandar",
"Ahmed Hendawy",
"Matthias Kayser",
"Bin Yang",
"Jürgen Beyerer"
] | https://openaccess.thecvf.com/content/WACV2023/html/Guirguis_Towards_Discriminative_and_Transferable_One-Stage_Few-Shot_Object_Detectors_WACV_2023_paper.html | https://openaccess.thecvf.com/content/WACV2023/papers/Guirguis_Towards_Discriminative_and_Transferable_One-Stage_Few-Shot_Object_Detectors_WACV_2023_paper.pdf | https://openaccess.thecvf.com/content/WACV2023/supplemental/Guirguis_Towards_Discriminative_and_WACV_2023_supplemental.pdf | 2210.05783 | cvf | @InProceedings{Guirguis_2023_WACV,
author = {Guirguis, Karim and Abdelsamad, Mohamed and Eskandar, George and Hendawy, Ahmed and Kayser, Matthias and Yang, Bin and Beyerer, J\"urgen},
title = {Towards Discriminative and Transferable One-Stage Few-Shot Object Detectors},
booktitle = {Proceedings of th... | Recent object detection models have proved valuable for many robotics and manufacturing tasks, but they require large amounts of annotated data for each new class of objects they are trained for. Few-shot object detection (FSOD) aims to address this problem by learning novel classes given only a few samples of annotate... |
Mathur_LayerDoc_Layer-Wise_Extraction_of_Spatial_Hierarchical_Structure_in_Visually-Rich_Documents_WACV_2023_paper | LayerDoc: Layer-Wise Extraction of Spatial Hierarchical Structure in Visually-Rich Documents | [
"Puneet Mathur",
"Rajiv Jain",
"Ashutosh Mehra",
"Jiuxiang Gu",
"Franck Dernoncourt",
"Anandhavelu N.",
"Quan Tran",
"Verena Kaynig-Fittkau",
"Ani Nenkova",
"Dinesh Manocha",
"Vlad I. Morariu"
] | https://openaccess.thecvf.com/content/WACV2023/html/Mathur_LayerDoc_Layer-Wise_Extraction_of_Spatial_Hierarchical_Structure_in_Visually-Rich_Documents_WACV_2023_paper.html | https://openaccess.thecvf.com/content/WACV2023/papers/Mathur_LayerDoc_Layer-Wise_Extraction_of_Spatial_Hierarchical_Structure_in_Visually-Rich_Documents_WACV_2023_paper.pdf | https://openaccess.thecvf.com/content/WACV2023/supplemental/Mathur_LayerDoc_Layer-Wise_Extraction_WACV_2023_supplemental.pdf | null | null | @InProceedings{Mathur_2023_WACV,
author = {Mathur, Puneet and Jain, Rajiv and Mehra, Ashutosh and Gu, Jiuxiang and Dernoncourt, Franck and N., Anandhavelu and Tran, Quan and Kaynig-Fittkau, Verena and Nenkova, Ani and Manocha, Dinesh and Morariu, Vlad I.},
title = {LayerDoc: Layer-Wise Extraction of Spat... | Digital documents often contain images and scanned text. Parsing such visually-rich documents is a core task for workflow automation, but it remains challenging since most documents do not encode explicit layout information, e.g., how characters and words are grouped into boxes and ordered into larger semantic entities... |
Chen_SSSD_Self-Supervised_Self_Distillation_WACV_2023_paper | SSSD: Self-Supervised Self Distillation | [
"Wei-Chi Chen",
"Wei-Ta Chu"
] | https://openaccess.thecvf.com/content/WACV2023/html/Chen_SSSD_Self-Supervised_Self_Distillation_WACV_2023_paper.html | https://openaccess.thecvf.com/content/WACV2023/papers/Chen_SSSD_Self-Supervised_Self_Distillation_WACV_2023_paper.pdf | null | null | null | @InProceedings{Chen_2023_WACV,
author = {Chen, Wei-Chi and Chu, Wei-Ta},
title = {SSSD: Self-Supervised Self Distillation},
booktitle = {Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)},
month = {January},
year = {2023},
pages = {2770-2... | With labeled data, self distillation (SD) has been proposed to develop compact but effective models without a complex teacher model available in advance. Such approaches need labeled data to guide the self distillation process. Inspired by self-supervised (SS) learning, we propose a self-supervised self distillation (S... |
Keaton_CellTranspose_Few-Shot_Domain_Adaptation_for_Cellular_Instance_Segmentation_WACV_2023_paper | CellTranspose: Few-Shot Domain Adaptation for Cellular Instance Segmentation | [
"Matthew R. Keaton",
"Ram J. Zaveri",
"Gianfranco Doretto"
] | https://openaccess.thecvf.com/content/WACV2023/html/Keaton_CellTranspose_Few-Shot_Domain_Adaptation_for_Cellular_Instance_Segmentation_WACV_2023_paper.html | https://openaccess.thecvf.com/content/WACV2023/papers/Keaton_CellTranspose_Few-Shot_Domain_Adaptation_for_Cellular_Instance_Segmentation_WACV_2023_paper.pdf | null | 2212.14121 | title_snapshot | @InProceedings{Keaton_2023_WACV,
author = {Keaton, Matthew R. and Zaveri, Ram J. and Doretto, Gianfranco},
title = {CellTranspose: Few-Shot Domain Adaptation for Cellular Instance Segmentation},
booktitle = {Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)},
... | Automated cellular instance segmentation is a process utilized for accelerating biological research for the past two decades, and recent advancements have produced higher quality results with less effort from the biologist. Most current endeavors focus on completely cutting the researcher out of the picture by generati... |
Yang_Hard_To_Track_Objects_With_Irregular_Motions_and_Similar_Appearances_WACV_2023_paper | Hard To Track Objects With Irregular Motions and Similar Appearances? Make It Easier by Buffering the Matching Space | [
"Fan Yang",
"Shigeyuki Odashima",
"Shoichi Masui",
"Shan Jiang"
] | https://openaccess.thecvf.com/content/WACV2023/html/Yang_Hard_To_Track_Objects_With_Irregular_Motions_and_Similar_Appearances_WACV_2023_paper.html | https://openaccess.thecvf.com/content/WACV2023/papers/Yang_Hard_To_Track_Objects_With_Irregular_Motions_and_Similar_Appearances_WACV_2023_paper.pdf | https://openaccess.thecvf.com/content/WACV2023/supplemental/Yang_Hard_To_Track_WACV_2023_supplemental.pdf | 2211.14317 | cvf | @InProceedings{Yang_2023_WACV,
author = {Yang, Fan and Odashima, Shigeyuki and Masui, Shoichi and Jiang, Shan},
title = {Hard To Track Objects With Irregular Motions and Similar Appearances? Make It Easier by Buffering the Matching Space},
booktitle = {Proceedings of the IEEE/CVF Winter Conference on... | We propose a Cascaded Buffered IoU (C-BIoU) tracker to track multiple objects that have irregular motions and indistinguishable appearances. When appearance features are unreliable and geometric features are confused by irregular motions, applying conventional Multiple Object Tracking (MOT) methods may generate unsatis... |
Bera_Self_Supervised_Low_Dose_Computed_Tomography_Image_Denoising_Using_Invertible_WACV_2023_paper | Self Supervised Low Dose Computed Tomography Image Denoising Using Invertible Network Exploiting Inter Slice Congruence | [
"Sutanu Bera",
"Prabir Kumar Biswas"
] | https://openaccess.thecvf.com/content/WACV2023/html/Bera_Self_Supervised_Low_Dose_Computed_Tomography_Image_Denoising_Using_Invertible_WACV_2023_paper.html | https://openaccess.thecvf.com/content/WACV2023/papers/Bera_Self_Supervised_Low_Dose_Computed_Tomography_Image_Denoising_Using_Invertible_WACV_2023_paper.pdf | null | 2211.01618 | cvf | @InProceedings{Bera_2023_WACV,
author = {Bera, Sutanu and Biswas, Prabir Kumar},
title = {Self Supervised Low Dose Computed Tomography Image Denoising Using Invertible Network Exploiting Inter Slice Congruence},
booktitle = {Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vi... | The resurgence of deep neural networks has created an alternative pathway for low-dose computed tomography denoising by learning a nonlinear transformation function between low-dose CT (LDCT) and normal-dose CT (NDCT) image pairs. However, those paired LDCT and NDCT images are rarely available in the clinical environme... |
Aich_Leveraging_Local_Patch_Differences_in_Multi-Object_Scenes_for_Generative_Adversarial_WACV_2023_paper | Leveraging Local Patch Differences in Multi-Object Scenes for Generative Adversarial Attacks | [
"Abhishek Aich",
"Shasha Li",
"Chengyu Song",
"M. Salman Asif",
"Srikanth V. Krishnamurthy",
"Amit K. Roy-Chowdhury"
] | https://openaccess.thecvf.com/content/WACV2023/html/Aich_Leveraging_Local_Patch_Differences_in_Multi-Object_Scenes_for_Generative_Adversarial_WACV_2023_paper.html | https://openaccess.thecvf.com/content/WACV2023/papers/Aich_Leveraging_Local_Patch_Differences_in_Multi-Object_Scenes_for_Generative_Adversarial_WACV_2023_paper.pdf | https://openaccess.thecvf.com/content/WACV2023/supplemental/Aich_Leveraging_Local_Patch_WACV_2023_supplemental.pdf | 2209.09883 | cvf | @InProceedings{Aich_2023_WACV,
author = {Aich, Abhishek and Li, Shasha and Song, Chengyu and Asif, M. Salman and Krishnamurthy, Srikanth V. and Roy-Chowdhury, Amit K.},
title = {Leveraging Local Patch Differences in Multi-Object Scenes for Generative Adversarial Attacks},
booktitle = {Proceedings of ... | State-of-the-art generative model-based attacks against image classifiers overwhelmingly focus on single-object (ie., single dominant object) images. Different from such settings, we tackle a more practical problem of generating adversarial perturbations using multi-object (ie., multiple dominant objects) images as the... |
Sun_PRN_Panoptic_Refinement_Network_WACV_2023_paper | PRN: Panoptic Refinement Network | [
"Bo Sun",
"Jason Kuen",
"Zhe Lin",
"Philippos Mordohai",
"Simon Chen"
] | https://openaccess.thecvf.com/content/WACV2023/html/Sun_PRN_Panoptic_Refinement_Network_WACV_2023_paper.html | https://openaccess.thecvf.com/content/WACV2023/papers/Sun_PRN_Panoptic_Refinement_Network_WACV_2023_paper.pdf | https://openaccess.thecvf.com/content/WACV2023/supplemental/Sun_PRN_Panoptic_Refinement_WACV_2023_supplemental.pdf | null | null | @InProceedings{Sun_2023_WACV,
author = {Sun, Bo and Kuen, Jason and Lin, Zhe and Mordohai, Philippos and Chen, Simon},
title = {PRN: Panoptic Refinement Network},
booktitle = {Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)},
month = {January},
year... | Panoptic segmentation is the task of uniquely assigning every pixel in an image to either a semantic label or an individual object instance, generating a coherent and complete scene description. Many current panoptic segmentation methods, however, predict masks of semantic classes and object instances in separate branc... |
Taherkhani_Controllable_3D_Generative_Adversarial_Face_Model_via_Disentangling_Shape_and_WACV_2023_paper | Controllable 3D Generative Adversarial Face Model via Disentangling Shape and Appearance | [
"Fariborz Taherkhani",
"Aashish Rai",
"Quankai Gao",
"Shaunak Srivastava",
"Xuanbai Chen",
"Fernando de la Torre",
"Steven Song",
"Aayush Prakash",
"Daeil Kim"
] | https://openaccess.thecvf.com/content/WACV2023/html/Taherkhani_Controllable_3D_Generative_Adversarial_Face_Model_via_Disentangling_Shape_and_WACV_2023_paper.html | https://openaccess.thecvf.com/content/WACV2023/papers/Taherkhani_Controllable_3D_Generative_Adversarial_Face_Model_via_Disentangling_Shape_and_WACV_2023_paper.pdf | https://openaccess.thecvf.com/content/WACV2023/supplemental/Taherkhani_Controllable_3D_Generative_WACV_2023_supplemental.zip | 2208.14263 | cvf | @InProceedings{Taherkhani_2023_WACV,
author = {Taherkhani, Fariborz and Rai, Aashish and Gao, Quankai and Srivastava, Shaunak and Chen, Xuanbai and de la Torre, Fernando and Song, Steven and Prakash, Aayush and Kim, Daeil},
title = {Controllable 3D Generative Adversarial Face Model via Disentangling Shap... | 3D face modeling has been an active area of research in computer vision and computer graphics, fueling applications ranging from facial expression transfer in virtual avatars to synthetic data generation. Existing 3D deep learning generative models (e.g., VAE, GANs) allow generating compact face representations (both s... |
Chen_Self-Supervised_Monocular_Depth_Estimation_Solving_the_Edge-Fattening_Problem_WACV_2023_paper | Self-Supervised Monocular Depth Estimation: Solving the Edge-Fattening Problem | [
"Xingyu Chen",
"Ruonan Zhang",
"Ji Jiang",
"Yan Wang",
"Ge Li",
"Thomas H. Li"
] | https://openaccess.thecvf.com/content/WACV2023/html/Chen_Self-Supervised_Monocular_Depth_Estimation_Solving_the_Edge-Fattening_Problem_WACV_2023_paper.html | https://openaccess.thecvf.com/content/WACV2023/papers/Chen_Self-Supervised_Monocular_Depth_Estimation_Solving_the_Edge-Fattening_Problem_WACV_2023_paper.pdf | null | 2210.00411 | cvf | @InProceedings{Chen_2023_WACV,
author = {Chen, Xingyu and Zhang, Ruonan and Jiang, Ji and Wang, Yan and Li, Ge and Li, Thomas H.},
title = {Self-Supervised Monocular Depth Estimation: Solving the Edge-Fattening Problem},
booktitle = {Proceedings of the IEEE/CVF Winter Conference on Applications of Co... | Self-supervised monocular depth estimation (MDE) models universally suffer from the notorious edge-fattening issue. Triplet loss, popular for metric learning, has made a great success in many computer vision tasks. In this paper, we redesign the patch-based triplet loss in MDE to alleviate the ubiquitous edge-fattening... |
Petrovai_MonoDVPS_A_Self-Supervised_Monocular_Depth_Estimation_Approach_to_Depth-Aware_Video_WACV_2023_paper | MonoDVPS: A Self-Supervised Monocular Depth Estimation Approach to Depth-Aware Video Panoptic Segmentation | [
"Andra Petrovai",
"Sergiu Nedevschi"
] | https://openaccess.thecvf.com/content/WACV2023/html/Petrovai_MonoDVPS_A_Self-Supervised_Monocular_Depth_Estimation_Approach_to_Depth-Aware_Video_WACV_2023_paper.html | https://openaccess.thecvf.com/content/WACV2023/papers/Petrovai_MonoDVPS_A_Self-Supervised_Monocular_Depth_Estimation_Approach_to_Depth-Aware_Video_WACV_2023_paper.pdf | https://openaccess.thecvf.com/content/WACV2023/supplemental/Petrovai_MonoDVPS_A_Self-Supervised_WACV_2023_supplemental.pdf | 2210.07577 | cvf | @InProceedings{Petrovai_2023_WACV,
author = {Petrovai, Andra and Nedevschi, Sergiu},
title = {MonoDVPS: A Self-Supervised Monocular Depth Estimation Approach to Depth-Aware Video Panoptic Segmentation},
booktitle = {Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (WAC... | Depth-aware video panoptic segmentation tackles the inverse projection problem of restoring panoptic 3D point clouds from video sequences, where the 3D points are augmented with semantic classes and temporally consistent instance identifiers. We propose a novel solution with a multi-task network that performs monocular... |
Varga_Wavelength-Aware_2D_Convolutions_for_Hyperspectral_Imaging_WACV_2023_paper | Wavelength-Aware 2D Convolutions for Hyperspectral Imaging | [
"Leon Amadeus Varga",
"Martin Messmer",
"Nuri Benbarka",
"Andreas Zell"
] | https://openaccess.thecvf.com/content/WACV2023/html/Varga_Wavelength-Aware_2D_Convolutions_for_Hyperspectral_Imaging_WACV_2023_paper.html | https://openaccess.thecvf.com/content/WACV2023/papers/Varga_Wavelength-Aware_2D_Convolutions_for_Hyperspectral_Imaging_WACV_2023_paper.pdf | https://openaccess.thecvf.com/content/WACV2023/supplemental/Varga_Wavelength-Aware_2D_Convolutions_WACV_2023_supplemental.pdf | 2209.03136 | cvf | @InProceedings{Varga_2023_WACV,
author = {Varga, Leon Amadeus and Messmer, Martin and Benbarka, Nuri and Zell, Andreas},
title = {Wavelength-Aware 2D Convolutions for Hyperspectral Imaging},
booktitle = {Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)},
mon... | Deep Learning could drastically boost the classification accuracy for Hyperspectral Imaging (HSI). Still, the training on the mostly small hyperspectral data sets is not trivial. Two key challenges are the large channel dimension of the recordings and the incompatibility between cameras of different manufacturers. By i... |
Pang_Contrastive_Losses_Are_Natural_Criteria_for_Unsupervised_Video_Summarization_WACV_2023_paper | Contrastive Losses Are Natural Criteria for Unsupervised Video Summarization | [
"Zongshang Pang",
"Yuta Nakashima",
"Mayu Otani",
"Hajime Nagahara"
] | https://openaccess.thecvf.com/content/WACV2023/html/Pang_Contrastive_Losses_Are_Natural_Criteria_for_Unsupervised_Video_Summarization_WACV_2023_paper.html | https://openaccess.thecvf.com/content/WACV2023/papers/Pang_Contrastive_Losses_Are_Natural_Criteria_for_Unsupervised_Video_Summarization_WACV_2023_paper.pdf | https://openaccess.thecvf.com/content/WACV2023/supplemental/Pang_Contrastive_Losses_Are_WACV_2023_supplemental.pdf | 2211.10056 | cvf | @InProceedings{Pang_2023_WACV,
author = {Pang, Zongshang and Nakashima, Yuta and Otani, Mayu and Nagahara, Hajime},
title = {Contrastive Losses Are Natural Criteria for Unsupervised Video Summarization},
booktitle = {Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (WA... | Video summarization aims to select a most informative subset of frames in a video to facilitate efficient video browsing. Unsupervised methods usually rely on heuristic training objectives such as diversity and representativeness. However, such methods need to bootstrap the online-generated summaries to compute the obj... |
Popovic_Spatially_Multi-Conditional_Image_Generation_WACV_2023_paper | Spatially Multi-Conditional Image Generation | [
"Nikola Popović",
"Ritika Chakraborty",
"Danda Pani Paudel",
"Thomas Probst",
"Luc Van Gool"
] | https://openaccess.thecvf.com/content/WACV2023/html/Popovic_Spatially_Multi-Conditional_Image_Generation_WACV_2023_paper.html | https://openaccess.thecvf.com/content/WACV2023/papers/Popovic_Spatially_Multi-Conditional_Image_Generation_WACV_2023_paper.pdf | https://openaccess.thecvf.com/content/WACV2023/supplemental/Popovic_Spatially_Multi-Conditional_Image_WACV_2023_supplemental.pdf | 2203.13812 | title_snapshot | @InProceedings{Popovic_2023_WACV,
author = {Popovi\'c, Nikola and Chakraborty, Ritika and Paudel, Danda Pani and Probst, Thomas and Van Gool, Luc},
title = {Spatially Multi-Conditional Image Generation},
booktitle = {Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (WA... | In most scenarios, conditional image generation can be thought of as an inversion of the image understanding process. Since generic image understanding involves solving multiple tasks, it is natural to aim at generating images via multi conditioning. However, multi-conditional image generation is a very challenging pro... |
VS_Towards_Online_Domain_Adaptive_Object_Detection_WACV_2023_paper | Towards Online Domain Adaptive Object Detection | [
"Vibashan VS",
"Poojan Oza",
"Vishal M. Patel"
] | https://openaccess.thecvf.com/content/WACV2023/html/VS_Towards_Online_Domain_Adaptive_Object_Detection_WACV_2023_paper.html | https://openaccess.thecvf.com/content/WACV2023/papers/VS_Towards_Online_Domain_Adaptive_Object_Detection_WACV_2023_paper.pdf | null | 2204.05289 | cvf | @InProceedings{VS_2023_WACV,
author = {VS, Vibashan and Oza, Poojan and Patel, Vishal M.},
title = {Towards Online Domain Adaptive Object Detection},
booktitle = {Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)},
month = {January},
year = {2023... | Existing object detection models assume both the training and test data are sampled from the same source domain. This assumption does not hold true when these detectors are deployed in real-world applications, where they encounter new visual domains. Unsupervised Domain Adaptation (UDA) methods are generally employed t... |
Risser-Maroix_What_Can_We_Learn_by_Predicting_Accuracy_WACV_2023_paper | What Can We Learn by Predicting Accuracy? | [
"Olivier Risser-Maroix",
"Benjamin Chamand"
] | https://openaccess.thecvf.com/content/WACV2023/html/Risser-Maroix_What_Can_We_Learn_by_Predicting_Accuracy_WACV_2023_paper.html | https://openaccess.thecvf.com/content/WACV2023/papers/Risser-Maroix_What_Can_We_Learn_by_Predicting_Accuracy_WACV_2023_paper.pdf | null | 2208.01358 | cvf | @InProceedings{Risser-Maroix_2023_WACV,
author = {Risser-Maroix, Olivier and Chamand, Benjamin},
title = {What Can We Learn by Predicting Accuracy?},
booktitle = {Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)},
month = {January},
year = {2023... | This paper seeks to answer the following question: "What can we learn by predicting accuracy?". Indeed, classification is one of the most popular tasks in machine learning, and many loss functions have been developed to maximize this non-differentiable objective function. Unlike past work on loss function design, which... |
Yamashita_nLMVS-Net_Deep_Non-Lambertian_Multi-View_Stereo_WACV_2023_paper | nLMVS-Net: Deep Non-Lambertian Multi-View Stereo | [
"Kohei Yamashita",
"Yuto Enyo",
"Shohei Nobuhara",
"Ko Nishino"
] | https://openaccess.thecvf.com/content/WACV2023/html/Yamashita_nLMVS-Net_Deep_Non-Lambertian_Multi-View_Stereo_WACV_2023_paper.html | https://openaccess.thecvf.com/content/WACV2023/papers/Yamashita_nLMVS-Net_Deep_Non-Lambertian_Multi-View_Stereo_WACV_2023_paper.pdf | https://openaccess.thecvf.com/content/WACV2023/supplemental/Yamashita_nLMVS-Net_Deep_Non-Lambertian_WACV_2023_supplemental.zip | 2207.11876 | title_snapshot | @InProceedings{Yamashita_2023_WACV,
author = {Yamashita, Kohei and Enyo, Yuto and Nobuhara, Shohei and Nishino, Ko},
title = {nLMVS-Net: Deep Non-Lambertian Multi-View Stereo},
booktitle = {Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)},
month = {Janu... | We introduce a novel multi-view stereo (MVS) method that can simultaneously recover not just per-pixel depth but also surface normals, together with the reflectance of textureless, complex non-Lambertian surfaces captured under known but natural illumination. Our key idea is to formulate MVS as an end-to-end learnable ... |
Hwang_Ev-NeRF_Event_Based_Neural_Radiance_Field_WACV_2023_paper | Ev-NeRF: Event Based Neural Radiance Field | [
"Inwoo Hwang",
"Junho Kim",
"Young Min Kim"
] | https://openaccess.thecvf.com/content/WACV2023/html/Hwang_Ev-NeRF_Event_Based_Neural_Radiance_Field_WACV_2023_paper.html | https://openaccess.thecvf.com/content/WACV2023/papers/Hwang_Ev-NeRF_Event_Based_Neural_Radiance_Field_WACV_2023_paper.pdf | https://openaccess.thecvf.com/content/WACV2023/supplemental/Hwang_Ev-NeRF_Event_Based_WACV_2023_supplemental.pdf | 2206.12455 | title_snapshot | @InProceedings{Hwang_2023_WACV,
author = {Hwang, Inwoo and Kim, Junho and Kim, Young Min},
title = {Ev-NeRF: Event Based Neural Radiance Field},
booktitle = {Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)},
month = {January},
year = {2023},
... | We present Ev-NeRF, a Neural Radiance Field derived from event data. While event cameras can measure subtle brightness changes in high frame rates, the measurements in low lighting or extreme motion suffer from significant domain discrepancy with complex noise. As a result, the performance of event-based vision tasks d... |
Li_Jointly_Learning_Band_Selection_and_Filter_Array_Design_for_Hyperspectral_WACV_2023_paper | Jointly Learning Band Selection and Filter Array Design for Hyperspectral Imaging | [
"Ke Li",
"Dengxin Dai",
"Luc Van Gool"
] | https://openaccess.thecvf.com/content/WACV2023/html/Li_Jointly_Learning_Band_Selection_and_Filter_Array_Design_for_Hyperspectral_WACV_2023_paper.html | https://openaccess.thecvf.com/content/WACV2023/papers/Li_Jointly_Learning_Band_Selection_and_Filter_Array_Design_for_Hyperspectral_WACV_2023_paper.pdf | https://openaccess.thecvf.com/content/WACV2023/supplemental/Li_Jointly_Learning_Band_WACV_2023_supplemental.pdf | null | null | @InProceedings{Li_2023_WACV,
author = {Li, Ke and Dai, Dengxin and Van Gool, Luc},
title = {Jointly Learning Band Selection and Filter Array Design for Hyperspectral Imaging},
booktitle = {Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)},
month = {Janua... | A single-shot multispectral camera equipped with an optimized color filter array (CFA) has the potential to deliver a fast and low-cost hyperspectral (HS) imaging system. Previous solutions are largely restricted to designing demosaicing algorithms for fixed CFAs - be it the Bayer color pattern or evenly-spaced spectra... |
Kobs_InDiReCT_Language-Guided_Zero-Shot_Deep_Metric_Learning_for_Images_WACV_2023_paper | InDiReCT: Language-Guided Zero-Shot Deep Metric Learning for Images | [
"Konstantin Kobs",
"Michael Steininger",
"Andreas Hotho"
] | https://openaccess.thecvf.com/content/WACV2023/html/Kobs_InDiReCT_Language-Guided_Zero-Shot_Deep_Metric_Learning_for_Images_WACV_2023_paper.html | https://openaccess.thecvf.com/content/WACV2023/papers/Kobs_InDiReCT_Language-Guided_Zero-Shot_Deep_Metric_Learning_for_Images_WACV_2023_paper.pdf | https://openaccess.thecvf.com/content/WACV2023/supplemental/Kobs_InDiReCT_Language-Guided_Zero-Shot_WACV_2023_supplemental.pdf | 2211.12760 | cvf | @InProceedings{Kobs_2023_WACV,
author = {Kobs, Konstantin and Steininger, Michael and Hotho, Andreas},
title = {InDiReCT: Language-Guided Zero-Shot Deep Metric Learning for Images},
booktitle = {Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)},
month = ... | Common Deep Metric Learning (DML) datasets specify only one notion of similarity, e.g., two images in the Cars196 dataset are deemed similar if they show the same car model. We argue that depending on the application, users of image retrieval systems have different and changing similarity notions that should be incorpo... |
Yap_Cut-Paste_Consistency_Learning_for_Semi-Supervised_Lesion_Segmentation_WACV_2023_paper | Cut-Paste Consistency Learning for Semi-Supervised Lesion Segmentation | [
"Boon Peng Yap",
"Beng Koon Ng"
] | https://openaccess.thecvf.com/content/WACV2023/html/Yap_Cut-Paste_Consistency_Learning_for_Semi-Supervised_Lesion_Segmentation_WACV_2023_paper.html | https://openaccess.thecvf.com/content/WACV2023/papers/Yap_Cut-Paste_Consistency_Learning_for_Semi-Supervised_Lesion_Segmentation_WACV_2023_paper.pdf | null | 2210.00191 | cvf | @InProceedings{Yap_2023_WACV,
author = {Yap, Boon Peng and Ng, Beng Koon},
title = {Cut-Paste Consistency Learning for Semi-Supervised Lesion Segmentation},
booktitle = {Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)},
month = {January},
year ... | Semi-supervised learning has the potential to improve the data-efficiency of training data-hungry deep neural networks, which is especially important for medical image analysis tasks where labeled data is scarce. In this work, we present a simple semi-supervised learning method for lesion segmentation tasks based on th... |
Rahman_Medical_Image_Segmentation_via_Cascaded_Attention_Decoding_WACV_2023_paper | Medical Image Segmentation via Cascaded Attention Decoding | [
"Md Mostafijur Rahman",
"Radu Marculescu"
] | https://openaccess.thecvf.com/content/WACV2023/html/Rahman_Medical_Image_Segmentation_via_Cascaded_Attention_Decoding_WACV_2023_paper.html | https://openaccess.thecvf.com/content/WACV2023/papers/Rahman_Medical_Image_Segmentation_via_Cascaded_Attention_Decoding_WACV_2023_paper.pdf | https://openaccess.thecvf.com/content/WACV2023/supplemental/Rahman_Medical_Image_Segmentation_WACV_2023_supplemental.zip | null | null | @InProceedings{Rahman_2023_WACV,
author = {Rahman, Md Mostafijur and Marculescu, Radu},
title = {Medical Image Segmentation via Cascaded Attention Decoding},
booktitle = {Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)},
month = {January},
year ... | Transformers have shown great promise in medical image segmentation due to their ability to capture long-range dependencies through self-attention. However, they lack the ability to learn the local (contextual) relations among pixels. Previous works try to overcome this problem by embedding convolutional layers either ... |
Tan_Visualizing_Global_Explanations_of_Point_Cloud_DNNs_WACV_2023_paper | Visualizing Global Explanations of Point Cloud DNNs | [
"Hanxiao Tan"
] | https://openaccess.thecvf.com/content/WACV2023/html/Tan_Visualizing_Global_Explanations_of_Point_Cloud_DNNs_WACV_2023_paper.html | https://openaccess.thecvf.com/content/WACV2023/papers/Tan_Visualizing_Global_Explanations_of_Point_Cloud_DNNs_WACV_2023_paper.pdf | https://openaccess.thecvf.com/content/WACV2023/supplemental/Tan_Visualizing_Global_Explanations_WACV_2023_supplemental.pdf | 2203.09505 | cvf | @InProceedings{Tan_2023_WACV,
author = {Tan, Hanxiao},
title = {Visualizing Global Explanations of Point Cloud DNNs},
booktitle = {Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)},
month = {January},
year = {2023},
pages = {4741-4750}
} | So far, few researchers have targeted the explainability of point cloud neural networks. Part of the explainability methods are not directly applicable to those networks due to the structural specifics. In this work, we show that Activation Maximization (AM) with traditional pixel-wise regularizations fails to generate... |
Nunez_LCS_Learning_Compressible_Subspaces_for_Efficient_Adaptive_Real-Time_Network_Compression_WACV_2023_paper | LCS: Learning Compressible Subspaces for Efficient, Adaptive, Real-Time Network Compression at Inference Time | [
"Elvis Nunez",
"Maxwell Horton",
"Anish Prabhu",
"Anurag Ranjan",
"Ali Farhadi",
"Mohammad Rastegari"
] | https://openaccess.thecvf.com/content/WACV2023/html/Nunez_LCS_Learning_Compressible_Subspaces_for_Efficient_Adaptive_Real-Time_Network_Compression_WACV_2023_paper.html | https://openaccess.thecvf.com/content/WACV2023/papers/Nunez_LCS_Learning_Compressible_Subspaces_for_Efficient_Adaptive_Real-Time_Network_Compression_WACV_2023_paper.pdf | https://openaccess.thecvf.com/content/WACV2023/supplemental/Nunez_LCS_Learning_Compressible_WACV_2023_supplemental.pdf | 2110.04252 | title_judge | @InProceedings{Nunez_2023_WACV,
author = {Nunez, Elvis and Horton, Maxwell and Prabhu, Anish and Ranjan, Anurag and Farhadi, Ali and Rastegari, Mohammad},
title = {LCS: Learning Compressible Subspaces for Efficient, Adaptive, Real-Time Network Compression at Inference Time},
booktitle = {Proceedings ... | When deploying deep neural networks (DNNs) to a device, it is traditionally assumed that available computational resources (compute, memory, and power) remain static. However, real-world computing systems do not always provide stable resource guarantees. Computational resources need to be conserved when load from other... |
Valanarasu_Fine-Context_Shadow_Detection_Using_Shadow_Removal_WACV_2023_paper | Fine-Context Shadow Detection Using Shadow Removal | [
"Jeya Maria Jose Valanarasu",
"Vishal M. Patel"
] | https://openaccess.thecvf.com/content/WACV2023/html/Valanarasu_Fine-Context_Shadow_Detection_Using_Shadow_Removal_WACV_2023_paper.html | https://openaccess.thecvf.com/content/WACV2023/papers/Valanarasu_Fine-Context_Shadow_Detection_Using_Shadow_Removal_WACV_2023_paper.pdf | https://openaccess.thecvf.com/content/WACV2023/supplemental/Valanarasu_Fine-Context_Shadow_Detection_WACV_2023_supplemental.pdf | 2109.09609 | cvf | @InProceedings{Valanarasu_2023_WACV,
author = {Valanarasu, Jeya Maria Jose and Patel, Vishal M.},
title = {Fine-Context Shadow Detection Using Shadow Removal},
booktitle = {Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)},
month = {January},
year ... | Current shadow detection methods perform poorly when detecting shadow regions that are small, unclear or have blurry edges. In this work, we attempt to address this problem on two fronts. First, we propose a Fine Context-aware Shadow Detection Network (FCSD-Net), where we constraint the receptive field size and focus o... |
Verelst_Spatial_Consistency_Loss_for_Training_Multi-Label_Classifiers_From_Single-Label_Annotations_WACV_2023_paper | Spatial Consistency Loss for Training Multi-Label Classifiers From Single-Label Annotations | [
"Thomas Verelst",
"Paul K. Rubenstein",
"Marcin Eichner",
"Tinne Tuytelaars",
"Maxim Berman"
] | https://openaccess.thecvf.com/content/WACV2023/html/Verelst_Spatial_Consistency_Loss_for_Training_Multi-Label_Classifiers_From_Single-Label_Annotations_WACV_2023_paper.html | https://openaccess.thecvf.com/content/WACV2023/papers/Verelst_Spatial_Consistency_Loss_for_Training_Multi-Label_Classifiers_From_Single-Label_Annotations_WACV_2023_paper.pdf | https://openaccess.thecvf.com/content/WACV2023/supplemental/Verelst_Spatial_Consistency_Loss_WACV_2023_supplemental.pdf | 2203.06127 | cvf | @InProceedings{Verelst_2023_WACV,
author = {Verelst, Thomas and Rubenstein, Paul K. and Eichner, Marcin and Tuytelaars, Tinne and Berman, Maxim},
title = {Spatial Consistency Loss for Training Multi-Label Classifiers From Single-Label Annotations},
booktitle = {Proceedings of the IEEE/CVF Winter Conf... | Multi-label image classification is more applicable 'in the wild' than single-label classification, as natural images usually contain multiple objects. However, exhaustively annotating images with every object of interest is costly and time-consuming. We train multi-label classifiers from datasets where each image is a... |
Sairam_ARUBA_An_Architecture-Agnostic_Balanced_Loss_for_Aerial_Object_Detection_WACV_2023_paper | ARUBA: An Architecture-Agnostic Balanced Loss for Aerial Object Detection | [
"Rebbapragada V. C. Sairam",
"Monish Keswani",
"Uttaran Sinha",
"Nishit Shah",
"Vineeth N. Balasubramanian"
] | https://openaccess.thecvf.com/content/WACV2023/html/Sairam_ARUBA_An_Architecture-Agnostic_Balanced_Loss_for_Aerial_Object_Detection_WACV_2023_paper.html | https://openaccess.thecvf.com/content/WACV2023/papers/Sairam_ARUBA_An_Architecture-Agnostic_Balanced_Loss_for_Aerial_Object_Detection_WACV_2023_paper.pdf | https://openaccess.thecvf.com/content/WACV2023/supplemental/Sairam_ARUBA_An_Architecture-Agnostic_WACV_2023_supplemental.pdf | 2210.04574 | cvf | @InProceedings{Sairam_2023_WACV,
author = {Sairam, Rebbapragada V. C. and Keswani, Monish and Sinha, Uttaran and Shah, Nishit and Balasubramanian, Vineeth N.},
title = {ARUBA: An Architecture-Agnostic Balanced Loss for Aerial Object Detection},
booktitle = {Proceedings of the IEEE/CVF Winter Conferen... | Deep neural networks tend to reciprocate the bias of their training dataset. In object detection, the bias exists in the form of various imbalances such as class, background-foreground, and object size. In this paper, we denote size of an object as the number of pixels it covers in an image and size imbalance as the ov... |
Georgescu_Multimodal_Multi-Head_Convolutional_Attention_With_Various_Kernel_Sizes_for_Medical_WACV_2023_paper | Multimodal Multi-Head Convolutional Attention With Various Kernel Sizes for Medical Image Super-Resolution | [
"Mariana-Iuliana Georgescu",
"Radu Tudor Ionescu",
"Andreea-Iuliana Miron",
"Olivian Savencu",
"Nicolae-Cătălin Ristea",
"Nicolae Verga",
"Fahad Shahbaz Khan"
] | https://openaccess.thecvf.com/content/WACV2023/html/Georgescu_Multimodal_Multi-Head_Convolutional_Attention_With_Various_Kernel_Sizes_for_Medical_WACV_2023_paper.html | https://openaccess.thecvf.com/content/WACV2023/papers/Georgescu_Multimodal_Multi-Head_Convolutional_Attention_With_Various_Kernel_Sizes_for_Medical_WACV_2023_paper.pdf | https://openaccess.thecvf.com/content/WACV2023/supplemental/Georgescu_Multimodal_Multi-Head_Convolutional_WACV_2023_supplemental.pdf | 2204.04218 | cvf | @InProceedings{Georgescu_2023_WACV,
author = {Georgescu, Mariana-Iuliana and Ionescu, Radu Tudor and Miron, Andreea-Iuliana and Savencu, Olivian and Ristea, Nicolae-C\u{a}t\u{a}lin and Verga, Nicolae and Khan, Fahad Shahbaz},
title = {Multimodal Multi-Head Convolutional Attention With Various Kernel Size... | Super-resolving medical images can help physicians in providing more accurate diagnostics. In many situations, computed tomography (CT) or magnetic resonance imaging (MRI) techniques capture several scans (modes) during a single investigation, which can jointly be used (in a multimodal fashion) to further boost the qua... |
Mohamadi_FUSSL_Fuzzy_Uncertain_Self_Supervised_Learning_WACV_2023_paper | FUSSL: Fuzzy Uncertain Self Supervised Learning | [
"Salman Mohamadi",
"Gianfranco Doretto",
"Donald A. Adjeroh"
] | https://openaccess.thecvf.com/content/WACV2023/html/Mohamadi_FUSSL_Fuzzy_Uncertain_Self_Supervised_Learning_WACV_2023_paper.html | https://openaccess.thecvf.com/content/WACV2023/papers/Mohamadi_FUSSL_Fuzzy_Uncertain_Self_Supervised_Learning_WACV_2023_paper.pdf | null | 2210.15818 | cvf | @InProceedings{Mohamadi_2023_WACV,
author = {Mohamadi, Salman and Doretto, Gianfranco and Adjeroh, Donald A.},
title = {FUSSL: Fuzzy Uncertain Self Supervised Learning},
booktitle = {Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)},
month = {January},
... | Self supervised learning (SSL) has become a very successful technique to harness the power of unlabeled data, with no annotation effort. A number of developed approaches are evolving with the goal of outperforming supervised alternatives, which have been relatively successful. Similar to some other disciplines in deep ... |
Dadon_DDNeRF_Depth_Distribution_Neural_Radiance_Fields_WACV_2023_paper | DDNeRF: Depth Distribution Neural Radiance Fields | [
"David Dadon",
"Ohad Fried",
"Yacov Hel-Or"
] | https://openaccess.thecvf.com/content/WACV2023/html/Dadon_DDNeRF_Depth_Distribution_Neural_Radiance_Fields_WACV_2023_paper.html | https://openaccess.thecvf.com/content/WACV2023/papers/Dadon_DDNeRF_Depth_Distribution_Neural_Radiance_Fields_WACV_2023_paper.pdf | https://openaccess.thecvf.com/content/WACV2023/supplemental/Dadon_DDNeRF_Depth_Distribution_WACV_2023_supplemental.pdf | 2203.16626 | cvf | @InProceedings{Dadon_2023_WACV,
author = {Dadon, David and Fried, Ohad and Hel-Or, Yacov},
title = {DDNeRF: Depth Distribution Neural Radiance Fields},
booktitle = {Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)},
month = {January},
year = {20... | The field of implicit neural representation has made significant progress. Models such as neural radiance fields (NeRF), which uses relatively small neural networks, can represent high-quality scenes and achieve state-of-the-art results for novel view synthesis. Training these types of networks, however, is still compu... |
Laroche_Deep_Model-Based_Super-Resolution_With_Non-Uniform_Blur_WACV_2023_paper | Deep Model-Based Super-Resolution With Non-Uniform Blur | [
"Charles Laroche",
"Andrés Almansa",
"Matias Tassano"
] | https://openaccess.thecvf.com/content/WACV2023/html/Laroche_Deep_Model-Based_Super-Resolution_With_Non-Uniform_Blur_WACV_2023_paper.html | https://openaccess.thecvf.com/content/WACV2023/papers/Laroche_Deep_Model-Based_Super-Resolution_With_Non-Uniform_Blur_WACV_2023_paper.pdf | https://openaccess.thecvf.com/content/WACV2023/supplemental/Laroche_Deep_Model-Based_Super-Resolution_WACV_2023_supplemental.pdf | 2204.10109 | cvf | @InProceedings{Laroche_2023_WACV,
author = {Laroche, Charles and Almansa, Andr\'es and Tassano, Matias},
title = {Deep Model-Based Super-Resolution With Non-Uniform Blur},
booktitle = {Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)},
month = {January},... | We propose a state-of-the-art method for super-resolution with non-uniform blur. Single-image super-resolution methods seek to restore a high-resolution image from blurred, subsampled, and noisy measurements. Despite their impressive performance, existing techniques usually assume a uniform blur kernel. Hence, these te... |
Li_Progressive_Video_Summarization_via_Multimodal_Self-Supervised_Learning_WACV_2023_paper | Progressive Video Summarization via Multimodal Self-Supervised Learning | [
"Haopeng Li",
"Qiuhong Ke",
"Mingming Gong",
"Tom Drummond"
] | https://openaccess.thecvf.com/content/WACV2023/html/Li_Progressive_Video_Summarization_via_Multimodal_Self-Supervised_Learning_WACV_2023_paper.html | https://openaccess.thecvf.com/content/WACV2023/papers/Li_Progressive_Video_Summarization_via_Multimodal_Self-Supervised_Learning_WACV_2023_paper.pdf | https://openaccess.thecvf.com/content/WACV2023/supplemental/Li_Progressive_Video_Summarization_WACV_2023_supplemental.pdf | 2201.02494 | cvf | @InProceedings{Li_2023_WACV,
author = {Li, Haopeng and Ke, Qiuhong and Gong, Mingming and Drummond, Tom},
title = {Progressive Video Summarization via Multimodal Self-Supervised Learning},
booktitle = {Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)},
month... | Modern video summarization methods are based on deep neural networks that require a large amount of annotated data for training. However, existing datasets for video summarization are small-scale, easily leading to over-fitting of the deep models. Considering that the annotation of large-scale datasets is time-consumin... |
Verma_Pushing_the_Efficiency_Limit_Using_Structured_Sparse_Convolutions_WACV_2023_paper | Pushing the Efficiency Limit Using Structured Sparse Convolutions | [
"Vinay Kumar Verma",
"Nikhil Mehta",
"Shijing Si",
"Ricardo Henao",
"Lawrence Carin"
] | https://openaccess.thecvf.com/content/WACV2023/html/Verma_Pushing_the_Efficiency_Limit_Using_Structured_Sparse_Convolutions_WACV_2023_paper.html | https://openaccess.thecvf.com/content/WACV2023/papers/Verma_Pushing_the_Efficiency_Limit_Using_Structured_Sparse_Convolutions_WACV_2023_paper.pdf | https://openaccess.thecvf.com/content/WACV2023/supplemental/Verma_Pushing_the_Efficiency_WACV_2023_supplemental.pdf | 2210.12818 | cvf | @InProceedings{Verma_2023_WACV,
author = {Verma, Vinay Kumar and Mehta, Nikhil and Si, Shijing and Henao, Ricardo and Carin, Lawrence},
title = {Pushing the Efficiency Limit Using Structured Sparse Convolutions},
booktitle = {Proceedings of the IEEE/CVF Winter Conference on Applications of Computer V... | Weight pruning is among the most popular approaches for compressing deep convolutional neural networks. Recent work suggests that in a randomly initialized deep neural network, there exist sparse subnetworks that achieve performance comparable to the original network. Unfortunately, finding these subnetworks involves i... |
Ren_Robust_Real-World_Image_Enhancement_Based_on_Multi-Exposure_LDR_Images_WACV_2023_paper | Robust Real-World Image Enhancement Based on Multi-Exposure LDR Images | [
"Haoyu Ren",
"Yi Fan",
"Stephen Huang"
] | https://openaccess.thecvf.com/content/WACV2023/html/Ren_Robust_Real-World_Image_Enhancement_Based_on_Multi-Exposure_LDR_Images_WACV_2023_paper.html | https://openaccess.thecvf.com/content/WACV2023/papers/Ren_Robust_Real-World_Image_Enhancement_Based_on_Multi-Exposure_LDR_Images_WACV_2023_paper.pdf | https://openaccess.thecvf.com/content/WACV2023/supplemental/Ren_Robust_Real-World_Image_WACV_2023_supplemental.pdf | null | null | @InProceedings{Ren_2023_WACV,
author = {Ren, Haoyu and Fan, Yi and Huang, Stephen},
title = {Robust Real-World Image Enhancement Based on Multi-Exposure LDR Images},
booktitle = {Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)},
month = {January},
y... | Robust real-world image enhancement from multi-exposure low dynamic range (LDR) images is a challenging task due to the unexpected inconsistency among the input images, such as the large motion or various exposures. In this paper, we propose a novel end-to-end image enhancement network to solve this problem. After extr... |
Sahin_HOOT_Heavy_Occlusions_in_Object_Tracking_Benchmark_WACV_2023_paper | HOOT: Heavy Occlusions in Object Tracking Benchmark | [
"Gozde Sahin",
"Laurent Itti"
] | https://openaccess.thecvf.com/content/WACV2023/html/Sahin_HOOT_Heavy_Occlusions_in_Object_Tracking_Benchmark_WACV_2023_paper.html | https://openaccess.thecvf.com/content/WACV2023/papers/Sahin_HOOT_Heavy_Occlusions_in_Object_Tracking_Benchmark_WACV_2023_paper.pdf | https://openaccess.thecvf.com/content/WACV2023/supplemental/Sahin_HOOT_Heavy_Occlusions_WACV_2023_supplemental.pdf | null | null | @InProceedings{Sahin_2023_WACV,
author = {Sahin, Gozde and Itti, Laurent},
title = {HOOT: Heavy Occlusions in Object Tracking Benchmark},
booktitle = {Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)},
month = {January},
year = {2023},
pages... | In this paper, we present HOOT, the Heavy Occlusions in Object Tracking Benchmark, a new visual object tracking dataset aimed towards handling high occlusion scenarios for single-object tracking tasks. The benchmark consists of 581 high-quality videos, which have 436K frames densely annotated with rotated bounding boxe... |
Chen_Self-Attentive_Pooling_for_Efficient_Deep_Learning_WACV_2023_paper | Self-Attentive Pooling for Efficient Deep Learning | [
"Fang Chen",
"Gourav Datta",
"Souvik Kundu",
"Peter A. Beerel"
] | https://openaccess.thecvf.com/content/WACV2023/html/Chen_Self-Attentive_Pooling_for_Efficient_Deep_Learning_WACV_2023_paper.html | https://openaccess.thecvf.com/content/WACV2023/papers/Chen_Self-Attentive_Pooling_for_Efficient_Deep_Learning_WACV_2023_paper.pdf | https://openaccess.thecvf.com/content/WACV2023/supplemental/Chen_Self-Attentive_Pooling_for_WACV_2023_supplemental.pdf | 2209.07659 | cvf | @InProceedings{Chen_2023_WACV,
author = {Chen, Fang and Datta, Gourav and Kundu, Souvik and Beerel, Peter A.},
title = {Self-Attentive Pooling for Efficient Deep Learning},
booktitle = {Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)},
month = {January}... | Efficient custom pooling techniques that can aggressively trim the dimensions of a feature map for resource-constrained computer vision applications have recently gained significant traction. However, prior pooling works extract only the local context of the activation maps, limiting their effectiveness. In contrast, w... |
Jang_Self-Distilled_Self-Supervised_Representation_Learning_WACV_2023_paper | Self-Distilled Self-Supervised Representation Learning | [
"Jiho Jang",
"Seonhoon Kim",
"Kiyoon Yoo",
"Chaerin Kong",
"Jangho Kim",
"Nojun Kwak"
] | https://openaccess.thecvf.com/content/WACV2023/html/Jang_Self-Distilled_Self-Supervised_Representation_Learning_WACV_2023_paper.html | https://openaccess.thecvf.com/content/WACV2023/papers/Jang_Self-Distilled_Self-Supervised_Representation_Learning_WACV_2023_paper.pdf | null | 2111.12958 | cvf | @InProceedings{Jang_2023_WACV,
author = {Jang, Jiho and Kim, Seonhoon and Yoo, Kiyoon and Kong, Chaerin and Kim, Jangho and Kwak, Nojun},
title = {Self-Distilled Self-Supervised Representation Learning},
booktitle = {Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (WA... | State-of-the-art frameworks in self-supervised learning have recently shown that fully utilizing transformer-based models can lead to performance boost compared to conventional CNN models. Striving to maximize the mutual information of two views of an image, existing works apply a contrastive loss to the final represen... |
Kanakis_Composite_Learning_for_Robust_and_Effective_Dense_Predictions_WACV_2023_paper | Composite Learning for Robust and Effective Dense Predictions | [
"Menelaos Kanakis",
"Thomas E. Huang",
"David Brüggemann",
"Fisher Yu",
"Luc Van Gool"
] | https://openaccess.thecvf.com/content/WACV2023/html/Kanakis_Composite_Learning_for_Robust_and_Effective_Dense_Predictions_WACV_2023_paper.html | https://openaccess.thecvf.com/content/WACV2023/papers/Kanakis_Composite_Learning_for_Robust_and_Effective_Dense_Predictions_WACV_2023_paper.pdf | https://openaccess.thecvf.com/content/WACV2023/supplemental/Kanakis_Composite_Learning_for_WACV_2023_supplemental.pdf | 2210.07239 | title_snapshot | @InProceedings{Kanakis_2023_WACV,
author = {Kanakis, Menelaos and Huang, Thomas E. and Br\"uggemann, David and Yu, Fisher and Van Gool, Luc},
title = {Composite Learning for Robust and Effective Dense Predictions},
booktitle = {Proceedings of the IEEE/CVF Winter Conference on Applications of Computer... | Multi-task learning promises better model generalization on a target task by jointly optimizing it with an auxiliary task. However, the current practice requires additional labeling efforts for the auxiliary task, while not guaranteeing better model performance. In this paper, we find that jointly training a dense pred... |
Kang_Efficient_Skeleton-Based_Action_Recognition_via_Joint-Mapping_Strategies_WACV_2023_paper | Efficient Skeleton-Based Action Recognition via Joint-Mapping Strategies | [
"Min-Seok Kang",
"Dongoh Kang",
"HanSaem Kim"
] | https://openaccess.thecvf.com/content/WACV2023/html/Kang_Efficient_Skeleton-Based_Action_Recognition_via_Joint-Mapping_Strategies_WACV_2023_paper.html | https://openaccess.thecvf.com/content/WACV2023/papers/Kang_Efficient_Skeleton-Based_Action_Recognition_via_Joint-Mapping_Strategies_WACV_2023_paper.pdf | https://openaccess.thecvf.com/content/WACV2023/supplemental/Kang_Efficient_Skeleton-Based_Action_WACV_2023_supplemental.pdf | null | null | @InProceedings{Kang_2023_WACV,
author = {Kang, Min-Seok and Kang, Dongoh and Kim, HanSaem},
title = {Efficient Skeleton-Based Action Recognition via Joint-Mapping Strategies},
booktitle = {Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)},
month = {Janua... | Graph convolutional networks (GCNs) have brought remarkable progress in skeleton-based action recognition. However, high computational cost and large model size make models difficult to be applied in real-world embedded system. Specifically, GCN that is applied in automated surveillance system pre-require models such a... |
Kim_PointInverter_Point_Cloud_Reconstruction_and_Editing_via_a_Generative_Model_WACV_2023_paper | PointInverter: Point Cloud Reconstruction and Editing via a Generative Model With Shape Priors | [
"Jaeyeon Kim",
"Binh-Son Hua",
"Thanh Nguyen",
"Sai-Kit Yeung"
] | https://openaccess.thecvf.com/content/WACV2023/html/Kim_PointInverter_Point_Cloud_Reconstruction_and_Editing_via_a_Generative_Model_WACV_2023_paper.html | https://openaccess.thecvf.com/content/WACV2023/papers/Kim_PointInverter_Point_Cloud_Reconstruction_and_Editing_via_a_Generative_Model_WACV_2023_paper.pdf | null | 2211.08702 | cvf | @InProceedings{Kim_2023_WACV,
author = {Kim, Jaeyeon and Hua, Binh-Son and Nguyen, Thanh and Yeung, Sai-Kit},
title = {PointInverter: Point Cloud Reconstruction and Editing via a Generative Model With Shape Priors},
booktitle = {Proceedings of the IEEE/CVF Winter Conference on Applications of Compute... | In this paper, we propose a new method for mapping a 3D point cloud to the latent space of a 3D generative adversarial network. Our generative model for 3D point clouds is based on SP-GAN, a state-of-the-art sphere-guided 3D point cloud generator. We derive an efficient way to encode an input 3D point cloud to the late... |
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