paper_id stringlengths 35 126 | title stringlengths 15 141 | authors listlengths 1 18 | cvf_url stringlengths 92 183 | pdf_url stringlengths 93 184 | supp_url stringlengths 101 180 ⌀ | arxiv_id stringlengths 10 10 ⌀ | arxiv_id_source stringclasses 3
values | bibtex large_stringlengths 304 677 | abstract large_stringlengths 562 2.21k |
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Khandelwal_Segmentation-Grounded_Scene_Graph_Generation_ICCV_2021_paper | Segmentation-Grounded Scene Graph Generation | [
"Siddhesh Khandelwal",
"Mohammed Suhail",
"Leonid Sigal"
] | https://openaccess.thecvf.com/content/ICCV2021/html/Khandelwal_Segmentation-Grounded_Scene_Graph_Generation_ICCV_2021_paper.html | https://openaccess.thecvf.com/content/ICCV2021/papers/Khandelwal_Segmentation-Grounded_Scene_Graph_Generation_ICCV_2021_paper.pdf | https://openaccess.thecvf.com/content/ICCV2021/supplemental/Khandelwal_Segmentation-Grounded_Scene_Graph_ICCV_2021_supplemental.pdf | 2104.14207 | cvf | @InProceedings{Khandelwal_2021_ICCV,
author = {Khandelwal, Siddhesh and Suhail, Mohammed and Sigal, Leonid},
title = {Segmentation-Grounded Scene Graph Generation},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
month = {October},
year =... | Scene graph generation has emerged as an important problem in computer vision. While scene graphs provide a grounded representation of objects, their locations and relations in an image, they do so only at the granularity of proposal bounding boxes. In this work, we propose the first, to our knowledge, framework for pi... |
Arbelle_Detector-Free_Weakly_Supervised_Grounding_by_Separation_ICCV_2021_paper | Detector-Free Weakly Supervised Grounding by Separation | [
"Assaf Arbelle",
"Sivan Doveh",
"Amit Alfassy",
"Joseph Shtok",
"Guy Lev",
"Eli Schwartz",
"Hilde Kuehne",
"Hila Barak Levi",
"Prasanna Sattigeri",
"Rameswar Panda",
"Chun-Fu (Richard) Chen",
"Alex Bronstein",
"Kate Saenko",
"Shimon Ullman",
"Raja Giryes",
"Rogerio Feris",
"Leonid Ka... | https://openaccess.thecvf.com/content/ICCV2021/html/Arbelle_Detector-Free_Weakly_Supervised_Grounding_by_Separation_ICCV_2021_paper.html | https://openaccess.thecvf.com/content/ICCV2021/papers/Arbelle_Detector-Free_Weakly_Supervised_Grounding_by_Separation_ICCV_2021_paper.pdf | null | 2104.09829 | cvf | @InProceedings{Arbelle_2021_ICCV,
author = {Arbelle, Assaf and Doveh, Sivan and Alfassy, Amit and Shtok, Joseph and Lev, Guy and Schwartz, Eli and Kuehne, Hilde and Levi, Hila Barak and Sattigeri, Prasanna and Panda, Rameswar and Chen, Chun-Fu (Richard) and Bronstein, Alex and Saenko, Kate and Ullman, Shimon and... | Nowadays, there is an abundance of data involving images and surrounding free-form text weakly corresponding to those images. Weakly Supervised phrase-Grounding (WSG) deals with the task of using this data to learn to localize (or to ground) arbitrary text phrases in images without any additional annotations. However, ... |
Ayush_Geography-Aware_Self-Supervised_Learning_ICCV_2021_paper | Geography-Aware Self-Supervised Learning | [
"Kumar Ayush",
"Burak Uzkent",
"Chenlin Meng",
"Kumar Tanmay",
"Marshall Burke",
"David Lobell",
"Stefano Ermon"
] | https://openaccess.thecvf.com/content/ICCV2021/html/Ayush_Geography-Aware_Self-Supervised_Learning_ICCV_2021_paper.html | https://openaccess.thecvf.com/content/ICCV2021/papers/Ayush_Geography-Aware_Self-Supervised_Learning_ICCV_2021_paper.pdf | null | 2011.09980 | cvf | @InProceedings{Ayush_2021_ICCV,
author = {Ayush, Kumar and Uzkent, Burak and Meng, Chenlin and Tanmay, Kumar and Burke, Marshall and Lobell, David and Ermon, Stefano},
title = {Geography-Aware Self-Supervised Learning},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vis... | Contrastive learning methods have significantly narrowed the gap between supervised and unsupervised learning on computer vision tasks. In this paper, we explore their application to geo-located datasets, e.g. remote sensing, where unlabeled data is often abundant but labeled data is scarce. We first show that due to t... |
Zolfaghari_CrossCLR_Cross-Modal_Contrastive_Learning_for_Multi-Modal_Video_Representations_ICCV_2021_paper | CrossCLR: Cross-Modal Contrastive Learning for Multi-Modal Video Representations | [
"Mohammadreza Zolfaghari",
"Yi Zhu",
"Peter Gehler",
"Thomas Brox"
] | https://openaccess.thecvf.com/content/ICCV2021/html/Zolfaghari_CrossCLR_Cross-Modal_Contrastive_Learning_for_Multi-Modal_Video_Representations_ICCV_2021_paper.html | https://openaccess.thecvf.com/content/ICCV2021/papers/Zolfaghari_CrossCLR_Cross-Modal_Contrastive_Learning_for_Multi-Modal_Video_Representations_ICCV_2021_paper.pdf | https://openaccess.thecvf.com/content/ICCV2021/supplemental/Zolfaghari_CrossCLR_Cross-Modal_Contrastive_ICCV_2021_supplemental.pdf | 2109.14910 | cvf | @InProceedings{Zolfaghari_2021_ICCV,
author = {Zolfaghari, Mohammadreza and Zhu, Yi and Gehler, Peter and Brox, Thomas},
title = {CrossCLR: Cross-Modal Contrastive Learning for Multi-Modal Video Representations},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (IC... | Contrastive learning allows us to flexibly define powerful losses by contrasting positive pairs from sets of negative samples. Recently, the principle has also been used to learn cross-modal embeddings for video and text, yet without exploiting its full potential. In particular, previous losses do not take the intra-mo... |
Dong_Shape-Aware_Multi-Person_Pose_Estimation_From_Multi-View_Images_ICCV_2021_paper | Shape-Aware Multi-Person Pose Estimation From Multi-View Images | [
"Zijian Dong",
"Jie Song",
"Xu Chen",
"Chen Guo",
"Otmar Hilliges"
] | https://openaccess.thecvf.com/content/ICCV2021/html/Dong_Shape-Aware_Multi-Person_Pose_Estimation_From_Multi-View_Images_ICCV_2021_paper.html | https://openaccess.thecvf.com/content/ICCV2021/papers/Dong_Shape-Aware_Multi-Person_Pose_Estimation_From_Multi-View_Images_ICCV_2021_paper.pdf | null | 2110.02330 | cvf | @InProceedings{Dong_2021_ICCV,
author = {Dong, Zijian and Song, Jie and Chen, Xu and Guo, Chen and Hilliges, Otmar},
title = {Shape-Aware Multi-Person Pose Estimation From Multi-View Images},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
month =... | In this paper we contribute a simple yet effective approach for estimating 3D poses of multiple people from multi-view images. Our proposed coarse-to-fine pipeline first aggregates noisy 2D observations from multiple camera views into 3D space and then associates them into individual instances based on a confidence-awa... |
Son_Single_Image_Defocus_Deblurring_Using_Kernel-Sharing_Parallel_Atrous_Convolutions_ICCV_2021_paper | Single Image Defocus Deblurring Using Kernel-Sharing Parallel Atrous Convolutions | [
"Hyeongseok Son",
"Junyong Lee",
"Sunghyun Cho",
"Seungyong Lee"
] | https://openaccess.thecvf.com/content/ICCV2021/html/Son_Single_Image_Defocus_Deblurring_Using_Kernel-Sharing_Parallel_Atrous_Convolutions_ICCV_2021_paper.html | https://openaccess.thecvf.com/content/ICCV2021/papers/Son_Single_Image_Defocus_Deblurring_Using_Kernel-Sharing_Parallel_Atrous_Convolutions_ICCV_2021_paper.pdf | https://openaccess.thecvf.com/content/ICCV2021/supplemental/Son_Single_Image_Defocus_ICCV_2021_supplemental.pdf | 2108.09108 | cvf | @InProceedings{Son_2021_ICCV,
author = {Son, Hyeongseok and Lee, Junyong and Cho, Sunghyun and Lee, Seungyong},
title = {Single Image Defocus Deblurring Using Kernel-Sharing Parallel Atrous Convolutions},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
... | This paper proposes a novel deep learning approach for single image defocus deblurring based on inverse kernels. In a defocused image, the blur shapes are similar among pixels although the blur sizes can spatially vary. To utilize the property with inverse kernels, we exploit the observation that when only the size of ... |
Vargas_Time-Multiplexed_Coded_Aperture_Imaging_Learned_Coded_Aperture_and_Pixel_Exposures_ICCV_2021_paper | Time-Multiplexed Coded Aperture Imaging: Learned Coded Aperture and Pixel Exposures for Compressive Imaging Systems | [
"Edwin Vargas",
"Julien N. P. Martel",
"Gordon Wetzstein",
"Henry Arguello"
] | https://openaccess.thecvf.com/content/ICCV2021/html/Vargas_Time-Multiplexed_Coded_Aperture_Imaging_Learned_Coded_Aperture_and_Pixel_Exposures_ICCV_2021_paper.html | https://openaccess.thecvf.com/content/ICCV2021/papers/Vargas_Time-Multiplexed_Coded_Aperture_Imaging_Learned_Coded_Aperture_and_Pixel_Exposures_ICCV_2021_paper.pdf | https://openaccess.thecvf.com/content/ICCV2021/supplemental/Vargas_Time-Multiplexed_Coded_Aperture_ICCV_2021_supplemental.pdf | 2104.02820 | cvf | @InProceedings{Vargas_2021_ICCV,
author = {Vargas, Edwin and Martel, Julien N. P. and Wetzstein, Gordon and Arguello, Henry},
title = {Time-Multiplexed Coded Aperture Imaging: Learned Coded Aperture and Pixel Exposures for Compressive Imaging Systems},
booktitle = {Proceedings of the IEEE/CVF Interna... | Compressive imaging using coded apertures (CA) is a powerful technique that can be used to recover depth, light fields, hyperspectral images and other quantities from a single snapshot. The performance of compressive imaging systems based on CAs mostly depends on two factors: the properties of the mask's attenuation pa... |
Choi_Motion-Aware_Dynamic_Architecture_for_Efficient_Frame_Interpolation_ICCV_2021_paper | Motion-Aware Dynamic Architecture for Efficient Frame Interpolation | [
"Myungsub Choi",
"Suyoung Lee",
"Heewon Kim",
"Kyoung Mu Lee"
] | https://openaccess.thecvf.com/content/ICCV2021/html/Choi_Motion-Aware_Dynamic_Architecture_for_Efficient_Frame_Interpolation_ICCV_2021_paper.html | https://openaccess.thecvf.com/content/ICCV2021/papers/Choi_Motion-Aware_Dynamic_Architecture_for_Efficient_Frame_Interpolation_ICCV_2021_paper.pdf | https://openaccess.thecvf.com/content/ICCV2021/supplemental/Choi_Motion-Aware_Dynamic_Architecture_ICCV_2021_supplemental.pdf | null | null | @InProceedings{Choi_2021_ICCV,
author = {Choi, Myungsub and Lee, Suyoung and Kim, Heewon and Lee, Kyoung Mu},
title = {Motion-Aware Dynamic Architecture for Efficient Frame Interpolation},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
month = {O... | Video frame interpolation aims to synthesize accurate intermediate frames given a low-frame-rate video. While the quality of the generated frames is increasingly getting better, state-of-the-art models have become more and more computationally expensive. However, local regions with small or no motion can be easily inte... |
Kotar_Contrasting_Contrastive_Self-Supervised_Representation_Learning_Pipelines_ICCV_2021_paper | Contrasting Contrastive Self-Supervised Representation Learning Pipelines | [
"Klemen Kotar",
"Gabriel Ilharco",
"Ludwig Schmidt",
"Kiana Ehsani",
"Roozbeh Mottaghi"
] | https://openaccess.thecvf.com/content/ICCV2021/html/Kotar_Contrasting_Contrastive_Self-Supervised_Representation_Learning_Pipelines_ICCV_2021_paper.html | https://openaccess.thecvf.com/content/ICCV2021/papers/Kotar_Contrasting_Contrastive_Self-Supervised_Representation_Learning_Pipelines_ICCV_2021_paper.pdf | https://openaccess.thecvf.com/content/ICCV2021/supplemental/Kotar_Contrasting_Contrastive_Self-Supervised_ICCV_2021_supplemental.pdf | 2103.14005 | cvf | @InProceedings{Kotar_2021_ICCV,
author = {Kotar, Klemen and Ilharco, Gabriel and Schmidt, Ludwig and Ehsani, Kiana and Mottaghi, Roozbeh},
title = {Contrasting Contrastive Self-Supervised Representation Learning Pipelines},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer... | In the past few years, we have witnessed remarkable breakthroughs in self-supervised representation learning. Despite the success and adoption of representations learned through this paradigm, much is yet to be understood about how different training methods and datasets influence performance on downstream tasks. In th... |
Liu_Normalized_Human_Pose_Features_for_Human_Action_Video_Alignment_ICCV_2021_paper | Normalized Human Pose Features for Human Action Video Alignment | [
"Jingyuan Liu",
"Mingyi Shi",
"Qifeng Chen",
"Hongbo Fu",
"Chiew-Lan Tai"
] | https://openaccess.thecvf.com/content/ICCV2021/html/Liu_Normalized_Human_Pose_Features_for_Human_Action_Video_Alignment_ICCV_2021_paper.html | https://openaccess.thecvf.com/content/ICCV2021/papers/Liu_Normalized_Human_Pose_Features_for_Human_Action_Video_Alignment_ICCV_2021_paper.pdf | https://openaccess.thecvf.com/content/ICCV2021/supplemental/Liu_Normalized_Human_Pose_ICCV_2021_supplemental.zip | null | null | @InProceedings{Liu_2021_ICCV,
author = {Liu, Jingyuan and Shi, Mingyi and Chen, Qifeng and Fu, Hongbo and Tai, Chiew-Lan},
title = {Normalized Human Pose Features for Human Action Video Alignment},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
month... | We present a novel approach for extracting human pose features from human action videos. The goal is to let the pose features capture only the poses of the action while being invariant to other factors, including video backgrounds, the video subject's anthropometric characteristics and viewpoints. Such human pose featu... |
Xing_Learning_Hierarchical_Graph_Neural_Networks_for_Image_Clustering_ICCV_2021_paper | Learning Hierarchical Graph Neural Networks for Image Clustering | [
"Yifan Xing",
"Tong He",
"Tianjun Xiao",
"Yongxin Wang",
"Yuanjun Xiong",
"Wei Xia",
"David Wipf",
"Zheng Zhang",
"Stefano Soatto"
] | https://openaccess.thecvf.com/content/ICCV2021/html/Xing_Learning_Hierarchical_Graph_Neural_Networks_for_Image_Clustering_ICCV_2021_paper.html | https://openaccess.thecvf.com/content/ICCV2021/papers/Xing_Learning_Hierarchical_Graph_Neural_Networks_for_Image_Clustering_ICCV_2021_paper.pdf | https://openaccess.thecvf.com/content/ICCV2021/supplemental/Xing_Learning_Hierarchical_Graph_ICCV_2021_supplemental.pdf | 2107.01319 | cvf | @InProceedings{Xing_2021_ICCV,
author = {Xing, Yifan and He, Tong and Xiao, Tianjun and Wang, Yongxin and Xiong, Yuanjun and Xia, Wei and Wipf, David and Zhang, Zheng and Soatto, Stefano},
title = {Learning Hierarchical Graph Neural Networks for Image Clustering},
booktitle = {Proceedings of the IEEE... | We propose a hierarchical graph neural network (GNN) model that learns how to cluster a set of images into an unknown number of identities using a training set of images annotated with labels belonging to a disjoint set of identities. Our hierarchical GNN uses a novel approach to merge connected components predicted at... |
Yang_Indoor_Scene_Generation_From_a_Collection_of_Semantic-Segmented_Depth_Images_ICCV_2021_paper | Indoor Scene Generation From a Collection of Semantic-Segmented Depth Images | [
"Ming-Jia Yang",
"Yu-Xiao Guo",
"Bin Zhou",
"Xin Tong"
] | https://openaccess.thecvf.com/content/ICCV2021/html/Yang_Indoor_Scene_Generation_From_a_Collection_of_Semantic-Segmented_Depth_Images_ICCV_2021_paper.html | https://openaccess.thecvf.com/content/ICCV2021/papers/Yang_Indoor_Scene_Generation_From_a_Collection_of_Semantic-Segmented_Depth_Images_ICCV_2021_paper.pdf | https://openaccess.thecvf.com/content/ICCV2021/supplemental/Yang_Indoor_Scene_Generation_ICCV_2021_supplemental.pdf | 2108.09022 | cvf | @InProceedings{Yang_2021_ICCV,
author = {Yang, Ming-Jia and Guo, Yu-Xiao and Zhou, Bin and Tong, Xin},
title = {Indoor Scene Generation From a Collection of Semantic-Segmented Depth Images},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
month = ... | We present a method for creating 3D indoor scenes with a generative model learned from a collection of semantic-segmented depth images captured from different unknown scenes. Given a room with a specified size, our method automatically generates 3D objects in a room from a randomly sampled latent code. Different from e... |
Zauss_Keypoint_Communities_ICCV_2021_paper | Keypoint Communities | [
"Duncan Zauss",
"Sven Kreiss",
"Alexandre Alahi"
] | https://openaccess.thecvf.com/content/ICCV2021/html/Zauss_Keypoint_Communities_ICCV_2021_paper.html | https://openaccess.thecvf.com/content/ICCV2021/papers/Zauss_Keypoint_Communities_ICCV_2021_paper.pdf | null | 2110.00988 | cvf | @InProceedings{Zauss_2021_ICCV,
author = {Zauss, Duncan and Kreiss, Sven and Alahi, Alexandre},
title = {Keypoint Communities},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
month = {October},
year = {2021},
pages = {11057-11066... | We present a fast bottom-up method that jointly detects over 100 keypoints on humans or objects, also referred to as human/object pose estimation. We model all keypoints belonging to a human or an object --the pose-- as a graph and leverage insights from community detection to quantify the independence of keypoints. We... |
Wang_Can_Scale-Consistent_Monocular_Depth_Be_Learned_in_a_Self-Supervised_Scale-Invariant_ICCV_2021_paper | Can Scale-Consistent Monocular Depth Be Learned in a Self-Supervised Scale-Invariant Manner? | [
"Lijun Wang",
"Yifan Wang",
"Linzhao Wang",
"Yunlong Zhan",
"Ying Wang",
"Huchuan Lu"
] | https://openaccess.thecvf.com/content/ICCV2021/html/Wang_Can_Scale-Consistent_Monocular_Depth_Be_Learned_in_a_Self-Supervised_Scale-Invariant_ICCV_2021_paper.html | https://openaccess.thecvf.com/content/ICCV2021/papers/Wang_Can_Scale-Consistent_Monocular_Depth_Be_Learned_in_a_Self-Supervised_Scale-Invariant_ICCV_2021_paper.pdf | null | null | null | @InProceedings{Wang_2021_ICCV,
author = {Wang, Lijun and Wang, Yifan and Wang, Linzhao and Zhan, Yunlong and Wang, Ying and Lu, Huchuan},
title = {Can Scale-Consistent Monocular Depth Be Learned in a Self-Supervised Scale-Invariant Manner?},
booktitle = {Proceedings of the IEEE/CVF International Conf... | Geometric constraints are shown to enforce scale consistency and remedy the scale ambiguity issue in self-supervised monocular depth estimation. Meanwhile, scale-invariant losses focus on learning relative depth, leading to accurate relative depth prediction. To combine the best of both worlds, we learn scale-consisten... |
Ghiasi_Multi-Task_Self-Training_for_Learning_General_Representations_ICCV_2021_paper | Multi-Task Self-Training for Learning General Representations | [
"Golnaz Ghiasi",
"Barret Zoph",
"Ekin D. Cubuk",
"Quoc V. Le",
"Tsung-Yi Lin"
] | https://openaccess.thecvf.com/content/ICCV2021/html/Ghiasi_Multi-Task_Self-Training_for_Learning_General_Representations_ICCV_2021_paper.html | https://openaccess.thecvf.com/content/ICCV2021/papers/Ghiasi_Multi-Task_Self-Training_for_Learning_General_Representations_ICCV_2021_paper.pdf | https://openaccess.thecvf.com/content/ICCV2021/supplemental/Ghiasi_Multi-Task_Self-Training_for_ICCV_2021_supplemental.pdf | 2108.11353 | cvf | @InProceedings{Ghiasi_2021_ICCV,
author = {Ghiasi, Golnaz and Zoph, Barret and Cubuk, Ekin D. and Le, Quoc V. and Lin, Tsung-Yi},
title = {Multi-Task Self-Training for Learning General Representations},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
... | Despite the fast progress in training specialized models for various tasks, learning a single general model that works well for many tasks is still challenging for computer vision. Here we introduce multi-task self-training (MuST), which harnesses the knowledge in independent specialized teacher models (e.g., ImageNet ... |
Zheng_Adaptive_Unfolding_Total_Variation_Network_for_Low-Light_Image_Enhancement_ICCV_2021_paper | Adaptive Unfolding Total Variation Network for Low-Light Image Enhancement | [
"Chuanjun Zheng",
"Daming Shi",
"Wentian Shi"
] | https://openaccess.thecvf.com/content/ICCV2021/html/Zheng_Adaptive_Unfolding_Total_Variation_Network_for_Low-Light_Image_Enhancement_ICCV_2021_paper.html | https://openaccess.thecvf.com/content/ICCV2021/papers/Zheng_Adaptive_Unfolding_Total_Variation_Network_for_Low-Light_Image_Enhancement_ICCV_2021_paper.pdf | null | 2110.00984 | cvf | @InProceedings{Zheng_2021_ICCV,
author = {Zheng, Chuanjun and Shi, Daming and Shi, Wentian},
title = {Adaptive Unfolding Total Variation Network for Low-Light Image Enhancement},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
month = {October},
... | Real-world low-light images suffer from two main degradations, namely, inevitable noise and poor visibility. Since the noise exhibits different levels, its estimation has been implemented in recent works when enhancing low-light images from raw Bayer space. When it comes to sRGB color space, the noise estimation become... |
Yu_Training_Weakly_Supervised_Video_Frame_Interpolation_With_Events_ICCV_2021_paper | Training Weakly Supervised Video Frame Interpolation With Events | [
"Zhiyang Yu",
"Yu Zhang",
"Deyuan Liu",
"Dongqing Zou",
"Xijun Chen",
"Yebin Liu",
"Jimmy S. Ren"
] | https://openaccess.thecvf.com/content/ICCV2021/html/Yu_Training_Weakly_Supervised_Video_Frame_Interpolation_With_Events_ICCV_2021_paper.html | https://openaccess.thecvf.com/content/ICCV2021/papers/Yu_Training_Weakly_Supervised_Video_Frame_Interpolation_With_Events_ICCV_2021_paper.pdf | https://openaccess.thecvf.com/content/ICCV2021/supplemental/Yu_Training_Weakly_Supervised_ICCV_2021_supplemental.pdf | null | null | @InProceedings{Yu_2021_ICCV,
author = {Yu, Zhiyang and Zhang, Yu and Liu, Deyuan and Zou, Dongqing and Chen, Xijun and Liu, Yebin and Ren, Jimmy S.},
title = {Training Weakly Supervised Video Frame Interpolation With Events},
booktitle = {Proceedings of the IEEE/CVF International Conference on Comput... | Event-based video frame interpolation is promising as event cameras capture dense motion signals that can greatly facilitate motion-aware synthesis. However, training existing frameworks for this task requires high frame-rate videos with synchronized events, posing challenges to collect real training data. In this work... |
Pan_TransView_Inside_Outside_and_Across_the_Cropping_View_Boundaries_ICCV_2021_paper | TransView: Inside, Outside, and Across the Cropping View Boundaries | [
"Zhiyu Pan",
"Zhiguo Cao",
"Kewei Wang",
"Hao Lu",
"Weicai Zhong"
] | https://openaccess.thecvf.com/content/ICCV2021/html/Pan_TransView_Inside_Outside_and_Across_the_Cropping_View_Boundaries_ICCV_2021_paper.html | https://openaccess.thecvf.com/content/ICCV2021/papers/Pan_TransView_Inside_Outside_and_Across_the_Cropping_View_Boundaries_ICCV_2021_paper.pdf | null | null | null | @InProceedings{Pan_2021_ICCV,
author = {Pan, Zhiyu and Cao, Zhiguo and Wang, Kewei and Lu, Hao and Zhong, Weicai},
title = {TransView: Inside, Outside, and Across the Cropping View Boundaries},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
month ... | We show that relation modeling between visual elements matters in cropping view recommendation. Cropping view recommendation addresses the problem of image recomposition conditioned on the composition quality and the ranking of views (cropped sub-regions). This task is challenging because the visual difference is subtl... |
Song_Vis2Mesh_Efficient_Mesh_Reconstruction_From_Unstructured_Point_Clouds_of_Large_ICCV_2021_paper | Vis2Mesh: Efficient Mesh Reconstruction From Unstructured Point Clouds of Large Scenes With Learned Virtual View Visibility | [
"Shuang Song",
"Zhaopeng Cui",
"Rongjun Qin"
] | https://openaccess.thecvf.com/content/ICCV2021/html/Song_Vis2Mesh_Efficient_Mesh_Reconstruction_From_Unstructured_Point_Clouds_of_Large_ICCV_2021_paper.html | https://openaccess.thecvf.com/content/ICCV2021/papers/Song_Vis2Mesh_Efficient_Mesh_Reconstruction_From_Unstructured_Point_Clouds_of_Large_ICCV_2021_paper.pdf | https://openaccess.thecvf.com/content/ICCV2021/supplemental/Song_Vis2Mesh_Efficient_Mesh_ICCV_2021_supplemental.pdf | 2108.08378 | cvf | @InProceedings{Song_2021_ICCV,
author = {Song, Shuang and Cui, Zhaopeng and Qin, Rongjun},
title = {Vis2Mesh: Efficient Mesh Reconstruction From Unstructured Point Clouds of Large Scenes With Learned Virtual View Visibility},
booktitle = {Proceedings of the IEEE/CVF International Conference on Comput... | We present a novel framework for mesh reconstruction from unstructured point clouds by taking advantage of the learned visibility of the 3D points in the virtual views and traditional graph-cut based mesh generation. Specifically, we first propose a three-step network that explicitly employs depth completion for visibi... |
Cozzolino_ID-Reveal_Identity-Aware_DeepFake_Video_Detection_ICCV_2021_paper | ID-Reveal: Identity-Aware DeepFake Video Detection | [
"Davide Cozzolino",
"Andreas Rössler",
"Justus Thies",
"Matthias Nießner",
"Luisa Verdoliva"
] | https://openaccess.thecvf.com/content/ICCV2021/html/Cozzolino_ID-Reveal_Identity-Aware_DeepFake_Video_Detection_ICCV_2021_paper.html | https://openaccess.thecvf.com/content/ICCV2021/papers/Cozzolino_ID-Reveal_Identity-Aware_DeepFake_Video_Detection_ICCV_2021_paper.pdf | https://openaccess.thecvf.com/content/ICCV2021/supplemental/Cozzolino_ID-Reveal_Identity-Aware_DeepFake_ICCV_2021_supplemental.pdf | 2012.02512 | title_snapshot | @InProceedings{Cozzolino_2021_ICCV,
author = {Cozzolino, Davide and R\"ossler, Andreas and Thies, Justus and Nie{\ss}ner, Matthias and Verdoliva, Luisa},
title = {ID-Reveal: Identity-Aware DeepFake Video Detection},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision ... | A major challenge in DeepFake forgery detection is that state-of-the-art algorithms are mostly trained to detect a specific fake method. As a result, these approaches show poor generalization across different types of facial manipulations, e.g., from face swapping to facial reenactment. To this end, we introduce ID-Rev... |
Shoshan_GAN-Control_Explicitly_Controllable_GANs_ICCV_2021_paper | GAN-Control: Explicitly Controllable GANs | [
"Alon Shoshan",
"Nadav Bhonker",
"Igor Kviatkovsky",
"Gérard Medioni"
] | https://openaccess.thecvf.com/content/ICCV2021/html/Shoshan_GAN-Control_Explicitly_Controllable_GANs_ICCV_2021_paper.html | https://openaccess.thecvf.com/content/ICCV2021/papers/Shoshan_GAN-Control_Explicitly_Controllable_GANs_ICCV_2021_paper.pdf | https://openaccess.thecvf.com/content/ICCV2021/supplemental/Shoshan_GAN-Control_Explicitly_Controllable_ICCV_2021_supplemental.pdf | 2101.02477 | title_snapshot | @InProceedings{Shoshan_2021_ICCV,
author = {Shoshan, Alon and Bhonker, Nadav and Kviatkovsky, Igor and Medioni, G\'erard},
title = {GAN-Control: Explicitly Controllable GANs},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
month = {October},
... | We present a framework for training GANs with explicit control over generated facial images. We are able to control the generated image by settings exact attributes such as age, pose, expression, etc. Most approaches for manipulating GAN-generated images achieve partial control by leveraging the latent space disentangl... |
Li_A_Closer_Look_at_Rotation-Invariant_Deep_Point_Cloud_Analysis_ICCV_2021_paper | A Closer Look at Rotation-Invariant Deep Point Cloud Analysis | [
"Feiran Li",
"Kent Fujiwara",
"Fumio Okura",
"Yasuyuki Matsushita"
] | https://openaccess.thecvf.com/content/ICCV2021/html/Li_A_Closer_Look_at_Rotation-Invariant_Deep_Point_Cloud_Analysis_ICCV_2021_paper.html | https://openaccess.thecvf.com/content/ICCV2021/papers/Li_A_Closer_Look_at_Rotation-Invariant_Deep_Point_Cloud_Analysis_ICCV_2021_paper.pdf | https://openaccess.thecvf.com/content/ICCV2021/supplemental/Li_A_Closer_Look_ICCV_2021_supplemental.pdf | null | null | @InProceedings{Li_2021_ICCV,
author = {Li, Feiran and Fujiwara, Kent and Okura, Fumio and Matsushita, Yasuyuki},
title = {A Closer Look at Rotation-Invariant Deep Point Cloud Analysis},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
month = {Octo... | We consider the deep point cloud analysis tasks where the inputs of the networks are randomly rotated. Recent progress in rotation-invariant point cloud analysis is mainly driven by converting point clouds into their respective canonical poses, and principal component analysis (PCA) is a practical tool to achieve this.... |
Stutz_Relating_Adversarially_Robust_Generalization_to_Flat_Minima_ICCV_2021_paper | Relating Adversarially Robust Generalization to Flat Minima | [
"David Stutz",
"Matthias Hein",
"Bernt Schiele"
] | https://openaccess.thecvf.com/content/ICCV2021/html/Stutz_Relating_Adversarially_Robust_Generalization_to_Flat_Minima_ICCV_2021_paper.html | https://openaccess.thecvf.com/content/ICCV2021/papers/Stutz_Relating_Adversarially_Robust_Generalization_to_Flat_Minima_ICCV_2021_paper.pdf | https://openaccess.thecvf.com/content/ICCV2021/supplemental/Stutz_Relating_Adversarially_Robust_ICCV_2021_supplemental.pdf | 2104.04448 | cvf | @InProceedings{Stutz_2021_ICCV,
author = {Stutz, David and Hein, Matthias and Schiele, Bernt},
title = {Relating Adversarially Robust Generalization to Flat Minima},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
month = {October},
year ... | Adversarial training (AT) has become the de-facto standard to obtain models robust against adversarial examples. However, AT exhibits severe robust overfitting: cross-entropy loss on adversarial examples, so-called robust loss, decreases continuously on training examples, while eventually increasing on test examples. I... |
Jin_Re-Energizing_Domain_Discriminator_With_Sample_Relabeling_for_Adversarial_Domain_Adaptation_ICCV_2021_paper | Re-Energizing Domain Discriminator With Sample Relabeling for Adversarial Domain Adaptation | [
"Xin Jin",
"Cuiling Lan",
"Wenjun Zeng",
"Zhibo Chen"
] | https://openaccess.thecvf.com/content/ICCV2021/html/Jin_Re-Energizing_Domain_Discriminator_With_Sample_Relabeling_for_Adversarial_Domain_Adaptation_ICCV_2021_paper.html | https://openaccess.thecvf.com/content/ICCV2021/papers/Jin_Re-Energizing_Domain_Discriminator_With_Sample_Relabeling_for_Adversarial_Domain_Adaptation_ICCV_2021_paper.pdf | https://openaccess.thecvf.com/content/ICCV2021/supplemental/Jin_Re-Energizing_Domain_Discriminator_ICCV_2021_supplemental.pdf | 2103.11661 | cvf | @InProceedings{Jin_2021_ICCV,
author = {Jin, Xin and Lan, Cuiling and Zeng, Wenjun and Chen, Zhibo},
title = {Re-Energizing Domain Discriminator With Sample Relabeling for Adversarial Domain Adaptation},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
... | Many unsupervised domain adaptation (UDA) methods exploit domain adversarial training to align the features to reduce domain gap, where a feature extractor is trained to fool a domain discriminator in order to have aligned feature distributions. The discrimination capability of the domain classifier w.r.t. the increasi... |
Guo_Learning_To_Adversarially_Blur_Visual_Object_Tracking_ICCV_2021_paper | Learning To Adversarially Blur Visual Object Tracking | [
"Qing Guo",
"Ziyi Cheng",
"Felix Juefei-Xu",
"Lei Ma",
"Xiaofei Xie",
"Yang Liu",
"Jianjun Zhao"
] | https://openaccess.thecvf.com/content/ICCV2021/html/Guo_Learning_To_Adversarially_Blur_Visual_Object_Tracking_ICCV_2021_paper.html | https://openaccess.thecvf.com/content/ICCV2021/papers/Guo_Learning_To_Adversarially_Blur_Visual_Object_Tracking_ICCV_2021_paper.pdf | https://openaccess.thecvf.com/content/ICCV2021/supplemental/Guo_Learning_To_Adversarially_ICCV_2021_supplemental.zip | 2107.12085 | title_snapshot | @InProceedings{Guo_2021_ICCV,
author = {Guo, Qing and Cheng, Ziyi and Juefei-Xu, Felix and Ma, Lei and Xie, Xiaofei and Liu, Yang and Zhao, Jianjun},
title = {Learning To Adversarially Blur Visual Object Tracking},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (... | Motion blur caused by the moving of the object or camera during the exposure can be a key challenge for visual object tracking, affecting tracking accuracy significantly. In this work, we explore the robustness of visual object trackers against motion blur from a new angle, i.e., adversarial blur attack (ABA). Our main... |
Chowdhury_Few-Shot_Image_Classification_Just_Use_a_Library_of_Pre-Trained_Feature_ICCV_2021_paper | Few-Shot Image Classification: Just Use a Library of Pre-Trained Feature Extractors and a Simple Classifier | [
"Arkabandhu Chowdhury",
"Mingchao Jiang",
"Swarat Chaudhuri",
"Chris Jermaine"
] | https://openaccess.thecvf.com/content/ICCV2021/html/Chowdhury_Few-Shot_Image_Classification_Just_Use_a_Library_of_Pre-Trained_Feature_ICCV_2021_paper.html | https://openaccess.thecvf.com/content/ICCV2021/papers/Chowdhury_Few-Shot_Image_Classification_Just_Use_a_Library_of_Pre-Trained_Feature_ICCV_2021_paper.pdf | https://openaccess.thecvf.com/content/ICCV2021/supplemental/Chowdhury_Few-Shot_Image_Classification_ICCV_2021_supplemental.zip | 2101.00562 | cvf | @InProceedings{Chowdhury_2021_ICCV,
author = {Chowdhury, Arkabandhu and Jiang, Mingchao and Chaudhuri, Swarat and Jermaine, Chris},
title = {Few-Shot Image Classification: Just Use a Library of Pre-Trained Feature Extractors and a Simple Classifier},
booktitle = {Proceedings of the IEEE/CVF Internati... | Recent papers have suggested that transfer learning can outperform sophisticated meta-learning methods for few-shot image classification. We take this hypothesis to its logical conclusion, and suggest the use of an ensemble of high-quality, pre-trained feature extractors for few-shot image classification. We show exper... |
Li_COMISR_Compression-Informed_Video_Super-Resolution_ICCV_2021_paper | COMISR: Compression-Informed Video Super-Resolution | [
"Yinxiao Li",
"Pengchong Jin",
"Feng Yang",
"Ce Liu",
"Ming-Hsuan Yang",
"Peyman Milanfar"
] | https://openaccess.thecvf.com/content/ICCV2021/html/Li_COMISR_Compression-Informed_Video_Super-Resolution_ICCV_2021_paper.html | https://openaccess.thecvf.com/content/ICCV2021/papers/Li_COMISR_Compression-Informed_Video_Super-Resolution_ICCV_2021_paper.pdf | null | 2105.01237 | cvf | @InProceedings{Li_2021_ICCV,
author = {Li, Yinxiao and Jin, Pengchong and Yang, Feng and Liu, Ce and Yang, Ming-Hsuan and Milanfar, Peyman},
title = {COMISR: Compression-Informed Video Super-Resolution},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
... | Most video super-resolution methods focus on restoring high-resolution video frames from low-resolution videos without taking into account compression. However, most videos on the web or mobile devices are compressed, and the compression can be severe when the bandwidth is limited. In this paper, we propose a new compr... |
Bulat_Bit-Mixer_Mixed-Precision_Networks_With_Runtime_Bit-Width_Selection_ICCV_2021_paper | Bit-Mixer: Mixed-Precision Networks With Runtime Bit-Width Selection | [
"Adrian Bulat",
"Georgios Tzimiropoulos"
] | https://openaccess.thecvf.com/content/ICCV2021/html/Bulat_Bit-Mixer_Mixed-Precision_Networks_With_Runtime_Bit-Width_Selection_ICCV_2021_paper.html | https://openaccess.thecvf.com/content/ICCV2021/papers/Bulat_Bit-Mixer_Mixed-Precision_Networks_With_Runtime_Bit-Width_Selection_ICCV_2021_paper.pdf | https://openaccess.thecvf.com/content/ICCV2021/supplemental/Bulat_Bit-Mixer_Mixed-Precision_Networks_ICCV_2021_supplemental.pdf | 2103.17267 | title_snapshot | @InProceedings{Bulat_2021_ICCV,
author = {Bulat, Adrian and Tzimiropoulos, Georgios},
title = {Bit-Mixer: Mixed-Precision Networks With Runtime Bit-Width Selection},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
month = {October},
year ... | Mixed-precision networks allow for a variable bit-width quantization for every layer in the network. A major limitation of existing work is that the bit-width for each layer must be predefined during training time. This allows little flexibility if the characteristics of the device on which the network is deployed chan... |
Liu_Light_Field_Saliency_Detection_With_Dual_Local_Graph_Learning_and_ICCV_2021_paper | Light Field Saliency Detection With Dual Local Graph Learning and Reciprocative Guidance | [
"Nian Liu",
"Wangbo Zhao",
"Dingwen Zhang",
"Junwei Han",
"Ling Shao"
] | https://openaccess.thecvf.com/content/ICCV2021/html/Liu_Light_Field_Saliency_Detection_With_Dual_Local_Graph_Learning_and_ICCV_2021_paper.html | https://openaccess.thecvf.com/content/ICCV2021/papers/Liu_Light_Field_Saliency_Detection_With_Dual_Local_Graph_Learning_and_ICCV_2021_paper.pdf | null | null | null | @InProceedings{Liu_2021_ICCV,
author = {Liu, Nian and Zhao, Wangbo and Zhang, Dingwen and Han, Junwei and Shao, Ling},
title = {Light Field Saliency Detection With Dual Local Graph Learning and Reciprocative Guidance},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Visi... | The application of light field data in salient object detection is becoming increasingly popular in recent years. The difficulty lies in how to effectively fuse the features within the focal stack and how to cooperate them with the feature of the all-focus image. Previous methods usually fuse focal stack features via c... |
Lam_Finding_Representative_Interpretations_on_Convolutional_Neural_Networks_ICCV_2021_paper | Finding Representative Interpretations on Convolutional Neural Networks | [
"Peter Cho-Ho Lam",
"Lingyang Chu",
"Maxim Torgonskiy",
"Jian Pei",
"Yong Zhang",
"Lanjun Wang"
] | https://openaccess.thecvf.com/content/ICCV2021/html/Lam_Finding_Representative_Interpretations_on_Convolutional_Neural_Networks_ICCV_2021_paper.html | https://openaccess.thecvf.com/content/ICCV2021/papers/Lam_Finding_Representative_Interpretations_on_Convolutional_Neural_Networks_ICCV_2021_paper.pdf | https://openaccess.thecvf.com/content/ICCV2021/supplemental/Lam_Finding_Representative_Interpretations_ICCV_2021_supplemental.pdf | 2108.06384 | cvf | @InProceedings{Lam_2021_ICCV,
author = {Lam, Peter Cho-Ho and Chu, Lingyang and Torgonskiy, Maxim and Pei, Jian and Zhang, Yong and Wang, Lanjun},
title = {Finding Representative Interpretations on Convolutional Neural Networks},
booktitle = {Proceedings of the IEEE/CVF International Conference on Co... | Interpreting the decision logic behind effective deep convolutional neural networks (CNN) on images complements the success of deep learning models. However, the existing methods can only interpret some specific decision logic on individual or a small number of images. To facilitate human understandability and generali... |
Wang_AINet_Association_Implantation_for_Superpixel_Segmentation_ICCV_2021_paper | AINet: Association Implantation for Superpixel Segmentation | [
"Yaxiong Wang",
"Yunchao Wei",
"Xueming Qian",
"Li Zhu",
"Yi Yang"
] | https://openaccess.thecvf.com/content/ICCV2021/html/Wang_AINet_Association_Implantation_for_Superpixel_Segmentation_ICCV_2021_paper.html | https://openaccess.thecvf.com/content/ICCV2021/papers/Wang_AINet_Association_Implantation_for_Superpixel_Segmentation_ICCV_2021_paper.pdf | null | 2101.10696 | cvf | @InProceedings{Wang_2021_ICCV,
author = {Wang, Yaxiong and Wei, Yunchao and Qian, Xueming and Zhu, Li and Yang, Yi},
title = {AINet: Association Implantation for Superpixel Segmentation},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
month = {Oc... | Recently, some approaches are proposed to harness deep convolutional networks to facilitate superpixel segmentation. The common practice is to first evenly divide the image into a pre-defined number of grids and then learn to associate each pixel with its surrounding grids. However, simply applying a series of convolut... |
Wang_An_Asynchronous_Kalman_Filter_for_Hybrid_Event_Cameras_ICCV_2021_paper | An Asynchronous Kalman Filter for Hybrid Event Cameras | [
"Ziwei Wang",
"Yonhon Ng",
"Cedric Scheerlinck",
"Robert Mahony"
] | https://openaccess.thecvf.com/content/ICCV2021/html/Wang_An_Asynchronous_Kalman_Filter_for_Hybrid_Event_Cameras_ICCV_2021_paper.html | https://openaccess.thecvf.com/content/ICCV2021/papers/Wang_An_Asynchronous_Kalman_Filter_for_Hybrid_Event_Cameras_ICCV_2021_paper.pdf | https://openaccess.thecvf.com/content/ICCV2021/supplemental/Wang_An_Asynchronous_Kalman_ICCV_2021_supplemental.pdf | 2012.05590 | cvf | @InProceedings{Wang_2021_ICCV,
author = {Wang, Ziwei and Ng, Yonhon and Scheerlinck, Cedric and Mahony, Robert},
title = {An Asynchronous Kalman Filter for Hybrid Event Cameras},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
month = {October},
... | Event cameras are ideally suited to capture HDR visual information without blur but perform poorly on static or slowly changing scenes. Conversely, conventional image sensors measure absolute intensity of slowly changing scenes effectively but do poorly on high dynamic range or quickly changing scenes. In this paper, w... |
Ranasinghe_Orthogonal_Projection_Loss_ICCV_2021_paper | Orthogonal Projection Loss | [
"Kanchana Ranasinghe",
"Muzammal Naseer",
"Munawar Hayat",
"Salman Khan",
"Fahad Shahbaz Khan"
] | https://openaccess.thecvf.com/content/ICCV2021/html/Ranasinghe_Orthogonal_Projection_Loss_ICCV_2021_paper.html | https://openaccess.thecvf.com/content/ICCV2021/papers/Ranasinghe_Orthogonal_Projection_Loss_ICCV_2021_paper.pdf | https://openaccess.thecvf.com/content/ICCV2021/supplemental/Ranasinghe_Orthogonal_Projection_Loss_ICCV_2021_supplemental.pdf | 2103.14021 | cvf | @InProceedings{Ranasinghe_2021_ICCV,
author = {Ranasinghe, Kanchana and Naseer, Muzammal and Hayat, Munawar and Khan, Salman and Khan, Fahad Shahbaz},
title = {Orthogonal Projection Loss},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
month = {O... | Deep neural networks have achieved remarkable performance on a range of classification tasks, with softmax cross-entropy (CE) loss emerging as the de-facto objective function. The CE loss encourages features of a class to have a higher projection score on the true class-vector compared to the negative classes. However,... |
Kim_Deep_Virtual_Markers_for_Articulated_3D_Shapes_ICCV_2021_paper | Deep Virtual Markers for Articulated 3D Shapes | [
"Hyomin Kim",
"Jungeon Kim",
"Jaewon Kam",
"Jaesik Park",
"Seungyong Lee"
] | https://openaccess.thecvf.com/content/ICCV2021/html/Kim_Deep_Virtual_Markers_for_Articulated_3D_Shapes_ICCV_2021_paper.html | https://openaccess.thecvf.com/content/ICCV2021/papers/Kim_Deep_Virtual_Markers_for_Articulated_3D_Shapes_ICCV_2021_paper.pdf | https://openaccess.thecvf.com/content/ICCV2021/supplemental/Kim_Deep_Virtual_Markers_ICCV_2021_supplemental.pdf | 2108.09000 | cvf | @InProceedings{Kim_2021_ICCV,
author = {Kim, Hyomin and Kim, Jungeon and Kam, Jaewon and Park, Jaesik and Lee, Seungyong},
title = {Deep Virtual Markers for Articulated 3D Shapes},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
month = {October},... | We propose deep virtual markers, a framework for estimating dense and accurate positional information for various types of 3D data. We design a concept and construct a framework that maps 3D points of 3D articulated models, like humans, into virtual marker labels. To realize the framework, we adopt a sparse convolution... |
Zhan_Achieving_On-Mobile_Real-Time_Super-Resolution_With_Neural_Architecture_and_Pruning_Search_ICCV_2021_paper | Achieving On-Mobile Real-Time Super-Resolution With Neural Architecture and Pruning Search | [
"Zheng Zhan",
"Yifan Gong",
"Pu Zhao",
"Geng Yuan",
"Wei Niu",
"Yushu Wu",
"Tianyun Zhang",
"Malith Jayaweera",
"David Kaeli",
"Bin Ren",
"Xue Lin",
"Yanzhi Wang"
] | https://openaccess.thecvf.com/content/ICCV2021/html/Zhan_Achieving_On-Mobile_Real-Time_Super-Resolution_With_Neural_Architecture_and_Pruning_Search_ICCV_2021_paper.html | https://openaccess.thecvf.com/content/ICCV2021/papers/Zhan_Achieving_On-Mobile_Real-Time_Super-Resolution_With_Neural_Architecture_and_Pruning_Search_ICCV_2021_paper.pdf | https://openaccess.thecvf.com/content/ICCV2021/supplemental/Zhan_Achieving_On-Mobile_Real-Time_ICCV_2021_supplemental.pdf | 2108.08910 | cvf | @InProceedings{Zhan_2021_ICCV,
author = {Zhan, Zheng and Gong, Yifan and Zhao, Pu and Yuan, Geng and Niu, Wei and Wu, Yushu and Zhang, Tianyun and Jayaweera, Malith and Kaeli, David and Ren, Bin and Lin, Xue and Wang, Yanzhi},
title = {Achieving On-Mobile Real-Time Super-Resolution With Neural Architectu... | Though recent years have witnessed remarkable progress in single image super-resolution (SISR) tasks with the prosperous development of deep neural networks (DNNs), the deep learning methods are confronted with the computation and memory consumption issues in practice, especially for resource-limited platforms such as ... |
Liu_One-Pass_Multi-View_Clustering_for_Large-Scale_Data_ICCV_2021_paper | One-Pass Multi-View Clustering for Large-Scale Data | [
"Jiyuan Liu",
"Xinwang Liu",
"Yuexiang Yang",
"Li Liu",
"Siqi Wang",
"Weixuan Liang",
"Jiangyong Shi"
] | https://openaccess.thecvf.com/content/ICCV2021/html/Liu_One-Pass_Multi-View_Clustering_for_Large-Scale_Data_ICCV_2021_paper.html | https://openaccess.thecvf.com/content/ICCV2021/papers/Liu_One-Pass_Multi-View_Clustering_for_Large-Scale_Data_ICCV_2021_paper.pdf | https://openaccess.thecvf.com/content/ICCV2021/supplemental/Liu_One-Pass_Multi-View_Clustering_ICCV_2021_supplemental.pdf | null | null | @InProceedings{Liu_2021_ICCV,
author = {Liu, Jiyuan and Liu, Xinwang and Yang, Yuexiang and Liu, Li and Wang, Siqi and Liang, Weixuan and Shi, Jiangyong},
title = {One-Pass Multi-View Clustering for Large-Scale Data},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Visio... | Existing non-negative matrix factorization based multi-view clustering algorithms compute multiple coefficient matrices respect to different data views, and learn a common consensus concurrently. The final partition is always obtained from the consensus with classical clustering techniques, such as k-means. However, th... |
Chen_Knowledge-Enriched_Distributional_Model_Inversion_Attacks_ICCV_2021_paper | Knowledge-Enriched Distributional Model Inversion Attacks | [
"Si Chen",
"Mostafa Kahla",
"Ruoxi Jia",
"Guo-Jun Qi"
] | https://openaccess.thecvf.com/content/ICCV2021/html/Chen_Knowledge-Enriched_Distributional_Model_Inversion_Attacks_ICCV_2021_paper.html | https://openaccess.thecvf.com/content/ICCV2021/papers/Chen_Knowledge-Enriched_Distributional_Model_Inversion_Attacks_ICCV_2021_paper.pdf | https://openaccess.thecvf.com/content/ICCV2021/supplemental/Chen_Knowledge-Enriched_Distributional_Model_ICCV_2021_supplemental.pdf | 2010.04092 | cvf | @InProceedings{Chen_2021_ICCV,
author = {Chen, Si and Kahla, Mostafa and Jia, Ruoxi and Qi, Guo-Jun},
title = {Knowledge-Enriched Distributional Model Inversion Attacks},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
month = {October},
year ... | Model inversion (MI) attacks are aimed at reconstructing training data from model parameters. Such attacks have triggered increasing concerns about privacy, especially given the growing number of online model repositories. However, existing MI attacks against deep neural networks (DNNs) have a large room for performanc... |
Fei_Z-Score_Normalization_Hubness_and_Few-Shot_Learning_ICCV_2021_paper | Z-Score Normalization, Hubness, and Few-Shot Learning | [
"Nanyi Fei",
"Yizhao Gao",
"Zhiwu Lu",
"Tao Xiang"
] | https://openaccess.thecvf.com/content/ICCV2021/html/Fei_Z-Score_Normalization_Hubness_and_Few-Shot_Learning_ICCV_2021_paper.html | https://openaccess.thecvf.com/content/ICCV2021/papers/Fei_Z-Score_Normalization_Hubness_and_Few-Shot_Learning_ICCV_2021_paper.pdf | null | null | null | @InProceedings{Fei_2021_ICCV,
author = {Fei, Nanyi and Gao, Yizhao and Lu, Zhiwu and Xiang, Tao},
title = {Z-Score Normalization, Hubness, and Few-Shot Learning},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
month = {October},
year = {... | The goal of few-shot learning (FSL) is to recognize a set of novel classes with only few labeled samples by exploiting a large set of abundant base class samples. Adopting a meta-learning framework, most recent FSL methods meta-learn a deep feature embedding network, and during inference classify novel class samples us... |
He_Dense_Interaction_Learning_for_Video-Based_Person_Re-Identification_ICCV_2021_paper | Dense Interaction Learning for Video-Based Person Re-Identification | [
"Tianyu He",
"Xin Jin",
"Xu Shen",
"Jianqiang Huang",
"Zhibo Chen",
"Xian-Sheng Hua"
] | https://openaccess.thecvf.com/content/ICCV2021/html/He_Dense_Interaction_Learning_for_Video-Based_Person_Re-Identification_ICCV_2021_paper.html | https://openaccess.thecvf.com/content/ICCV2021/papers/He_Dense_Interaction_Learning_for_Video-Based_Person_Re-Identification_ICCV_2021_paper.pdf | https://openaccess.thecvf.com/content/ICCV2021/supplemental/He_Dense_Interaction_Learning_ICCV_2021_supplemental.pdf | 2103.09013 | cvf | @InProceedings{He_2021_ICCV,
author = {He, Tianyu and Jin, Xin and Shen, Xu and Huang, Jianqiang and Chen, Zhibo and Hua, Xian-Sheng},
title = {Dense Interaction Learning for Video-Based Person Re-Identification},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (I... | Video-based person re-identification (re-ID) aims at matching the same person across video clips. Efficiently exploiting multi-scale fine-grained features while building the structural interaction among them is pivotal for its success. In this paper, we propose a hybrid framework, Dense Interaction Learning (DenseIL), ... |
Zhao_M3D-VTON_A_Monocular-to-3D_Virtual_Try-On_Network_ICCV_2021_paper | M3D-VTON: A Monocular-to-3D Virtual Try-On Network | [
"Fuwei Zhao",
"Zhenyu Xie",
"Michael Kampffmeyer",
"Haoye Dong",
"Songfang Han",
"Tianxiang Zheng",
"Tao Zhang",
"Xiaodan Liang"
] | https://openaccess.thecvf.com/content/ICCV2021/html/Zhao_M3D-VTON_A_Monocular-to-3D_Virtual_Try-On_Network_ICCV_2021_paper.html | https://openaccess.thecvf.com/content/ICCV2021/papers/Zhao_M3D-VTON_A_Monocular-to-3D_Virtual_Try-On_Network_ICCV_2021_paper.pdf | https://openaccess.thecvf.com/content/ICCV2021/supplemental/Zhao_M3D-VTON_A_Monocular-to-3D_ICCV_2021_supplemental.zip | 2108.05126 | title_snapshot | @InProceedings{Zhao_2021_ICCV,
author = {Zhao, Fuwei and Xie, Zhenyu and Kampffmeyer, Michael and Dong, Haoye and Han, Songfang and Zheng, Tianxiang and Zhang, Tao and Liang, Xiaodan},
title = {M3D-VTON: A Monocular-to-3D Virtual Try-On Network},
booktitle = {Proceedings of the IEEE/CVF International... | Virtual 3D try-on can provide an intuitive and realistic view for online shopping and has a huge potential commercial value. However, existing 3D virtual try-on methods mainly rely on annotated 3D human shapes and garment templates, which hinders their applications in practical scenarios. 2D virtual try-on approaches p... |
Chockler_Explanations_for_Occluded_Images_ICCV_2021_paper | Explanations for Occluded Images | [
"Hana Chockler",
"Daniel Kroening",
"Youcheng Sun"
] | https://openaccess.thecvf.com/content/ICCV2021/html/Chockler_Explanations_for_Occluded_Images_ICCV_2021_paper.html | https://openaccess.thecvf.com/content/ICCV2021/papers/Chockler_Explanations_for_Occluded_Images_ICCV_2021_paper.pdf | null | 2103.03622 | cvf | @InProceedings{Chockler_2021_ICCV,
author = {Chockler, Hana and Kroening, Daniel and Sun, Youcheng},
title = {Explanations for Occluded Images},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
month = {October},
year = {2021},
pages ... | Existing algorithms for explaining the output of image classifiers perform poorly on inputs where the object of interest is partially occluded. We present a novel, black-box algorithm for computing explanations that uses a principled approach based on causal theory. We have implemented the method in the DeepCover tool.... |
Zhang_Designing_a_Practical_Degradation_Model_for_Deep_Blind_Image_Super-Resolution_ICCV_2021_paper | Designing a Practical Degradation Model for Deep Blind Image Super-Resolution | [
"Kai Zhang",
"Jingyun Liang",
"Luc Van Gool",
"Radu Timofte"
] | https://openaccess.thecvf.com/content/ICCV2021/html/Zhang_Designing_a_Practical_Degradation_Model_for_Deep_Blind_Image_Super-Resolution_ICCV_2021_paper.html | https://openaccess.thecvf.com/content/ICCV2021/papers/Zhang_Designing_a_Practical_Degradation_Model_for_Deep_Blind_Image_Super-Resolution_ICCV_2021_paper.pdf | null | 2103.14006 | cvf | @InProceedings{Zhang_2021_ICCV,
author = {Zhang, Kai and Liang, Jingyun and Van Gool, Luc and Timofte, Radu},
title = {Designing a Practical Degradation Model for Deep Blind Image Super-Resolution},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
mont... | It is widely acknowledged that single image super-resolution (SISR) methods would not perform well if the assumed degradation model deviates from those in real images. Although several degradation models take additional factors into consideration, such as blur, they are still not effective enough to cover the diverse d... |
Teney_Unshuffling_Data_for_Improved_Generalization_in_Visual_Question_Answering_ICCV_2021_paper | Unshuffling Data for Improved Generalization in Visual Question Answering | [
"Damien Teney",
"Ehsan Abbasnejad",
"Anton van den Hengel"
] | https://openaccess.thecvf.com/content/ICCV2021/html/Teney_Unshuffling_Data_for_Improved_Generalization_in_Visual_Question_Answering_ICCV_2021_paper.html | https://openaccess.thecvf.com/content/ICCV2021/papers/Teney_Unshuffling_Data_for_Improved_Generalization_in_Visual_Question_Answering_ICCV_2021_paper.pdf | null | null | null | @InProceedings{Teney_2021_ICCV,
author = {Teney, Damien and Abbasnejad, Ehsan and van den Hengel, Anton},
title = {Unshuffling Data for Improved Generalization in Visual Question Answering},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
month = ... | Generalization beyond the training distribution is a core challenge in machine learning. The common practice of mixing and shuffling examples when training neural networks may not be optimal in this regard. We show that partitioning the data into well-chosen, non-i.i.d. subsets treated as multiple training environments... |
Hu_Architecture_Disentanglement_for_Deep_Neural_Networks_ICCV_2021_paper | Architecture Disentanglement for Deep Neural Networks | [
"Jie Hu",
"Liujuan Cao",
"Tong Tong",
"Qixiang Ye",
"Shengchuan Zhang",
"Ke Li",
"Feiyue Huang",
"Ling Shao",
"Rongrong Ji"
] | https://openaccess.thecvf.com/content/ICCV2021/html/Hu_Architecture_Disentanglement_for_Deep_Neural_Networks_ICCV_2021_paper.html | https://openaccess.thecvf.com/content/ICCV2021/papers/Hu_Architecture_Disentanglement_for_Deep_Neural_Networks_ICCV_2021_paper.pdf | https://openaccess.thecvf.com/content/ICCV2021/supplemental/Hu_Architecture_Disentanglement_for_ICCV_2021_supplemental.pdf | 2003.13268 | cvf | @InProceedings{Hu_2021_ICCV,
author = {Hu, Jie and Cao, Liujuan and Tong, Tong and Ye, Qixiang and Zhang, Shengchuan and Li, Ke and Huang, Feiyue and Shao, Ling and Ji, Rongrong},
title = {Architecture Disentanglement for Deep Neural Networks},
booktitle = {Proceedings of the IEEE/CVF International C... | Understanding the inner workings of deep neural networks (DNNs) is essential to provide trustworthy artificial intelligence techniques for practical applications. Existing studies typically involve linking semantic concepts to units or layers of DNNs, but fail to explain the inference process. In this paper, we introdu... |
Fang_Instances_As_Queries_ICCV_2021_paper | Instances As Queries | [
"Yuxin Fang",
"Shusheng Yang",
"Xinggang Wang",
"Yu Li",
"Chen Fang",
"Ying Shan",
"Bin Feng",
"Wenyu Liu"
] | https://openaccess.thecvf.com/content/ICCV2021/html/Fang_Instances_As_Queries_ICCV_2021_paper.html | https://openaccess.thecvf.com/content/ICCV2021/papers/Fang_Instances_As_Queries_ICCV_2021_paper.pdf | null | 2105.01928 | title_snapshot | @InProceedings{Fang_2021_ICCV,
author = {Fang, Yuxin and Yang, Shusheng and Wang, Xinggang and Li, Yu and Fang, Chen and Shan, Ying and Feng, Bin and Liu, Wenyu},
title = {Instances As Queries},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
month ... | We present QueryInst, a new perspective for instance segmentation. QueryInst is a multi-stage end-to-end system that treats instances of interest as learnable queries, enabling query based object detectors, e.g., Sparse R-CNN, to have strong instance segmentation performance. The attributes of instances such as categor... |
Zhou_Omni-GAN_On_the_Secrets_of_cGANs_and_Beyond_ICCV_2021_paper | Omni-GAN: On the Secrets of cGANs and Beyond | [
"Peng Zhou",
"Lingxi Xie",
"Bingbing Ni",
"Cong Geng",
"Qi Tian"
] | https://openaccess.thecvf.com/content/ICCV2021/html/Zhou_Omni-GAN_On_the_Secrets_of_cGANs_and_Beyond_ICCV_2021_paper.html | https://openaccess.thecvf.com/content/ICCV2021/papers/Zhou_Omni-GAN_On_the_Secrets_of_cGANs_and_Beyond_ICCV_2021_paper.pdf | https://openaccess.thecvf.com/content/ICCV2021/supplemental/Zhou_Omni-GAN_On_the_ICCV_2021_supplemental.pdf | 2011.13074 | title_snapshot | @InProceedings{Zhou_2021_ICCV,
author = {Zhou, Peng and Xie, Lingxi and Ni, Bingbing and Geng, Cong and Tian, Qi},
title = {Omni-GAN: On the Secrets of cGANs and Beyond},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
month = {October},
year ... | The conditional generative adversarial network (cGAN) is a powerful tool of generating high-quality images, but existing approaches mostly suffer unsatisfying performance or the risk of mode collapse. This paper presents Omni-GAN, a variant of cGAN that reveals the devil in designing a proper discriminator for training... |
Sakaridis_ACDC_The_Adverse_Conditions_Dataset_With_Correspondences_for_Semantic_Driving_ICCV_2021_paper | ACDC: The Adverse Conditions Dataset With Correspondences for Semantic Driving Scene Understanding | [
"Christos Sakaridis",
"Dengxin Dai",
"Luc Van Gool"
] | https://openaccess.thecvf.com/content/ICCV2021/html/Sakaridis_ACDC_The_Adverse_Conditions_Dataset_With_Correspondences_for_Semantic_Driving_ICCV_2021_paper.html | https://openaccess.thecvf.com/content/ICCV2021/papers/Sakaridis_ACDC_The_Adverse_Conditions_Dataset_With_Correspondences_for_Semantic_Driving_ICCV_2021_paper.pdf | https://openaccess.thecvf.com/content/ICCV2021/supplemental/Sakaridis_ACDC_The_Adverse_ICCV_2021_supplemental.pdf | 2104.13395 | cvf | @InProceedings{Sakaridis_2021_ICCV,
author = {Sakaridis, Christos and Dai, Dengxin and Van Gool, Luc},
title = {ACDC: The Adverse Conditions Dataset With Correspondences for Semantic Driving Scene Understanding},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (IC... | Level 5 autonomy for self-driving cars requires a robust visual perception system that can parse input images under any visual condition. However, existing semantic segmentation datasets are either dominated by images captured under normal conditions or are small in scale. To address this, we introduce ACDC, the Advers... |
Xiao_Improving_De-Raining_Generalization_via_Neural_Reorganization_ICCV_2021_paper | Improving De-Raining Generalization via Neural Reorganization | [
"Jie Xiao",
"Man Zhou",
"Xueyang Fu",
"Aiping Liu",
"Zheng-Jun Zha"
] | https://openaccess.thecvf.com/content/ICCV2021/html/Xiao_Improving_De-Raining_Generalization_via_Neural_Reorganization_ICCV_2021_paper.html | https://openaccess.thecvf.com/content/ICCV2021/papers/Xiao_Improving_De-Raining_Generalization_via_Neural_Reorganization_ICCV_2021_paper.pdf | https://openaccess.thecvf.com/content/ICCV2021/supplemental/Xiao_Improving_De-Raining_Generalization_ICCV_2021_supplemental.pdf | null | null | @InProceedings{Xiao_2021_ICCV,
author = {Xiao, Jie and Zhou, Man and Fu, Xueyang and Liu, Aiping and Zha, Zheng-Jun},
title = {Improving De-Raining Generalization via Neural Reorganization},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
month = ... | Most existing image de-raining networks could only learn fixed mapping rules between paired rainy/clean images on single synthetic dataset and then stay static for lifetime. However, since single synthetic dataset merely provides a partial view for the distribution of rain streaks, the deep models well trained on an in... |
Zhou_3D_Shape_Generation_and_Completion_Through_Point-Voxel_Diffusion_ICCV_2021_paper | 3D Shape Generation and Completion Through Point-Voxel Diffusion | [
"Linqi Zhou",
"Yilun Du",
"Jiajun Wu"
] | https://openaccess.thecvf.com/content/ICCV2021/html/Zhou_3D_Shape_Generation_and_Completion_Through_Point-Voxel_Diffusion_ICCV_2021_paper.html | https://openaccess.thecvf.com/content/ICCV2021/papers/Zhou_3D_Shape_Generation_and_Completion_Through_Point-Voxel_Diffusion_ICCV_2021_paper.pdf | https://openaccess.thecvf.com/content/ICCV2021/supplemental/Zhou_3D_Shape_Generation_ICCV_2021_supplemental.pdf | 2104.03670 | cvf | @InProceedings{Zhou_2021_ICCV,
author = {Zhou, Linqi and Du, Yilun and Wu, Jiajun},
title = {3D Shape Generation and Completion Through Point-Voxel Diffusion},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
month = {October},
year = {202... | We propose a novel approach for probabilistic generative modeling of 3D shapes. Unlike most existing models that learn to deterministically translate a latent vector to a shape, our model, Point-Voxel Diffusion (PVD), is a unified, probabilistic formulation for unconditional shape generation and conditional, multi-moda... |
Feng_Temporal_Knowledge_Consistency_for_Unsupervised_Visual_Representation_Learning_ICCV_2021_paper | Temporal Knowledge Consistency for Unsupervised Visual Representation Learning | [
"Weixin Feng",
"Yuanjiang Wang",
"Lihua Ma",
"Ye Yuan",
"Chi Zhang"
] | https://openaccess.thecvf.com/content/ICCV2021/html/Feng_Temporal_Knowledge_Consistency_for_Unsupervised_Visual_Representation_Learning_ICCV_2021_paper.html | https://openaccess.thecvf.com/content/ICCV2021/papers/Feng_Temporal_Knowledge_Consistency_for_Unsupervised_Visual_Representation_Learning_ICCV_2021_paper.pdf | https://openaccess.thecvf.com/content/ICCV2021/supplemental/Feng_Temporal_Knowledge_Consistency_ICCV_2021_supplemental.pdf | 2108.10668 | cvf | @InProceedings{Feng_2021_ICCV,
author = {Feng, Weixin and Wang, Yuanjiang and Ma, Lihua and Yuan, Ye and Zhang, Chi},
title = {Temporal Knowledge Consistency for Unsupervised Visual Representation Learning},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},... | The instance discrimination paradigm has become dominant in unsupervised learning. It always adopts a teacher-student framework, in which the teacher provides embedded knowledge as a supervision signal for the student. The student learns meaningful representations by enforcing instance spatial consistency with the view... |
Tian_Self-Conditioned_Probabilistic_Learning_of_Video_Rescaling_ICCV_2021_paper | Self-Conditioned Probabilistic Learning of Video Rescaling | [
"Yuan Tian",
"Guo Lu",
"Xiongkuo Min",
"Zhaohui Che",
"Guangtao Zhai",
"Guodong Guo",
"Zhiyong Gao"
] | https://openaccess.thecvf.com/content/ICCV2021/html/Tian_Self-Conditioned_Probabilistic_Learning_of_Video_Rescaling_ICCV_2021_paper.html | https://openaccess.thecvf.com/content/ICCV2021/papers/Tian_Self-Conditioned_Probabilistic_Learning_of_Video_Rescaling_ICCV_2021_paper.pdf | null | 2107.11639 | cvf | @InProceedings{Tian_2021_ICCV,
author = {Tian, Yuan and Lu, Guo and Min, Xiongkuo and Che, Zhaohui and Zhai, Guangtao and Guo, Guodong and Gao, Zhiyong},
title = {Self-Conditioned Probabilistic Learning of Video Rescaling},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer... | Bicubic downscaling is a prevalent technique used to reduce the video storage burden or to accelerate the downstream processing speed. However, the inverse upscaling step is non-trivial, and the downscaled video may also deteriorate the performance of downstream tasks. In this paper, we propose a self-conditioned proba... |
Ying_Unsupervised_Image_Generation_With_Infinite_Generative_Adversarial_Networks_ICCV_2021_paper | Unsupervised Image Generation With Infinite Generative Adversarial Networks | [
"Hui Ying",
"He Wang",
"Tianjia Shao",
"Yin Yang",
"Kun Zhou"
] | https://openaccess.thecvf.com/content/ICCV2021/html/Ying_Unsupervised_Image_Generation_With_Infinite_Generative_Adversarial_Networks_ICCV_2021_paper.html | https://openaccess.thecvf.com/content/ICCV2021/papers/Ying_Unsupervised_Image_Generation_With_Infinite_Generative_Adversarial_Networks_ICCV_2021_paper.pdf | https://openaccess.thecvf.com/content/ICCV2021/supplemental/Ying_Unsupervised_Image_Generation_ICCV_2021_supplemental.pdf | 2108.07975 | cvf | @InProceedings{Ying_2021_ICCV,
author = {Ying, Hui and Wang, He and Shao, Tianjia and Yang, Yin and Zhou, Kun},
title = {Unsupervised Image Generation With Infinite Generative Adversarial Networks},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
mont... | Image generation has been heavily investigated in computer vision, where one core research challenge is to generate images from arbitrarily complex distributions with little supervision. Generative Adversarial Networks (GANs) as an implicit approach have achieved great successes in this direction and therefore been emp... |
Chen_SGPA_Structure-Guided_Prior_Adaptation_for_Category-Level_6D_Object_Pose_Estimation_ICCV_2021_paper | SGPA: Structure-Guided Prior Adaptation for Category-Level 6D Object Pose Estimation | [
"Kai Chen",
"Qi Dou"
] | https://openaccess.thecvf.com/content/ICCV2021/html/Chen_SGPA_Structure-Guided_Prior_Adaptation_for_Category-Level_6D_Object_Pose_Estimation_ICCV_2021_paper.html | https://openaccess.thecvf.com/content/ICCV2021/papers/Chen_SGPA_Structure-Guided_Prior_Adaptation_for_Category-Level_6D_Object_Pose_Estimation_ICCV_2021_paper.pdf | https://openaccess.thecvf.com/content/ICCV2021/supplemental/Chen_SGPA_Structure-Guided_Prior_ICCV_2021_supplemental.pdf | null | null | @InProceedings{Chen_2021_ICCV,
author = {Chen, Kai and Dou, Qi},
title = {SGPA: Structure-Guided Prior Adaptation for Category-Level 6D Object Pose Estimation},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
month = {October},
year = {20... | Category-level 6D object pose estimation aims to predict the position and orientation for unseen objects, which plays a pillar role in many scenarios such as robotics and augmented reality. The significant intra-class variation is the bottleneck challenge in this task yet remains unsolved so far. In this paper, we take... |
He_Inferring_High-Resolution_Traffic_Accident_Risk_Maps_Based_on_Satellite_Imagery_ICCV_2021_paper | Inferring High-Resolution Traffic Accident Risk Maps Based on Satellite Imagery and GPS Trajectories | [
"Songtao He",
"Mohammad Amin Sadeghi",
"Sanjay Chawla",
"Mohammad Alizadeh",
"Hari Balakrishnan",
"Samuel Madden"
] | https://openaccess.thecvf.com/content/ICCV2021/html/He_Inferring_High-Resolution_Traffic_Accident_Risk_Maps_Based_on_Satellite_Imagery_ICCV_2021_paper.html | https://openaccess.thecvf.com/content/ICCV2021/papers/He_Inferring_High-Resolution_Traffic_Accident_Risk_Maps_Based_on_Satellite_Imagery_ICCV_2021_paper.pdf | null | null | null | @InProceedings{He_2021_ICCV,
author = {He, Songtao and Sadeghi, Mohammad Amin and Chawla, Sanjay and Alizadeh, Mohammad and Balakrishnan, Hari and Madden, Samuel},
title = {Inferring High-Resolution Traffic Accident Risk Maps Based on Satellite Imagery and GPS Trajectories},
booktitle = {Proceedings ... | Traffic accidents cost about 3% of the world's GDP and are the leading cause of death in children and young adults. Accident risk maps are useful tools to monitor and mitigate accident risk. We present a technique to generate high-resolution (5 meters) accident risk maps. At this high resolution, accidents are sparse a... |
Jang_Self-Supervised_Product_Quantization_for_Deep_Unsupervised_Image_Retrieval_ICCV_2021_paper | Self-Supervised Product Quantization for Deep Unsupervised Image Retrieval | [
"Young Kyun Jang",
"Nam Ik Cho"
] | https://openaccess.thecvf.com/content/ICCV2021/html/Jang_Self-Supervised_Product_Quantization_for_Deep_Unsupervised_Image_Retrieval_ICCV_2021_paper.html | https://openaccess.thecvf.com/content/ICCV2021/papers/Jang_Self-Supervised_Product_Quantization_for_Deep_Unsupervised_Image_Retrieval_ICCV_2021_paper.pdf | null | 2109.02244 | cvf | @InProceedings{Jang_2021_ICCV,
author = {Jang, Young Kyun and Cho, Nam Ik},
title = {Self-Supervised Product Quantization for Deep Unsupervised Image Retrieval},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
month = {October},
year = {2... | Supervised deep learning-based hash and vector quantization are enabling fast and large-scale image retrieval systems. By fully exploiting label annotations, they are achieving outstanding retrieval performances compared to the conventional methods. However, it is painstaking to assign labels precisely for a vast amoun... |
Cheng_On_Equivariant_and_Invariant_Learning_of_Object_Landmark_Representations_ICCV_2021_paper | On Equivariant and Invariant Learning of Object Landmark Representations | [
"Zezhou Cheng",
"Jong-Chyi Su",
"Subhransu Maji"
] | https://openaccess.thecvf.com/content/ICCV2021/html/Cheng_On_Equivariant_and_Invariant_Learning_of_Object_Landmark_Representations_ICCV_2021_paper.html | https://openaccess.thecvf.com/content/ICCV2021/papers/Cheng_On_Equivariant_and_Invariant_Learning_of_Object_Landmark_Representations_ICCV_2021_paper.pdf | https://openaccess.thecvf.com/content/ICCV2021/supplemental/Cheng_On_Equivariant_and_ICCV_2021_supplemental.pdf | 2006.14787 | cvf | @InProceedings{Cheng_2021_ICCV,
author = {Cheng, Zezhou and Su, Jong-Chyi and Maji, Subhransu},
title = {On Equivariant and Invariant Learning of Object Landmark Representations},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
month = {October},
... | Given a collection of images, humans are able to discover landmarks by modeling the shared geometric structure across instances. This idea of geometric equivariance has been widely used for the unsupervised discovery of object landmark representations. In this paper, we develop a simple and effective approach by combin... |
Jo_Rethinking_Deep_Image_Prior_for_Denoising_ICCV_2021_paper | Rethinking Deep Image Prior for Denoising | [
"Yeonsik Jo",
"Se Young Chun",
"Jonghyun Choi"
] | https://openaccess.thecvf.com/content/ICCV2021/html/Jo_Rethinking_Deep_Image_Prior_for_Denoising_ICCV_2021_paper.html | https://openaccess.thecvf.com/content/ICCV2021/papers/Jo_Rethinking_Deep_Image_Prior_for_Denoising_ICCV_2021_paper.pdf | https://openaccess.thecvf.com/content/ICCV2021/supplemental/Jo_Rethinking_Deep_Image_ICCV_2021_supplemental.pdf | 2108.12841 | cvf | @InProceedings{Jo_2021_ICCV,
author = {Jo, Yeonsik and Chun, Se Young and Choi, Jonghyun},
title = {Rethinking Deep Image Prior for Denoising},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
month = {October},
year = {2021},
pages ... | Deep image prior (DIP) serves as a good inductive bias for diverse inverse problems. Among them, denoising is known to be particularly challenging for the DIP due to noise fitting with the requirement of an early stopping. To address the issue, we first analyze the DIP by the notion of effective degrees of freedom (DF)... |
Buhler_VariTex_Variational_Neural_Face_Textures_ICCV_2021_paper | VariTex: Variational Neural Face Textures | [
"Marcel C. Bühler",
"Abhimitra Meka",
"Gengyan Li",
"Thabo Beeler",
"Otmar Hilliges"
] | https://openaccess.thecvf.com/content/ICCV2021/html/Buhler_VariTex_Variational_Neural_Face_Textures_ICCV_2021_paper.html | https://openaccess.thecvf.com/content/ICCV2021/papers/Buhler_VariTex_Variational_Neural_Face_Textures_ICCV_2021_paper.pdf | https://openaccess.thecvf.com/content/ICCV2021/supplemental/Buhler_VariTex_Variational_Neural_ICCV_2021_supplemental.zip | 2104.05988 | title_snapshot | @InProceedings{Buhler_2021_ICCV,
author = {B\"uhler, Marcel C. and Meka, Abhimitra and Li, Gengyan and Beeler, Thabo and Hilliges, Otmar},
title = {VariTex: Variational Neural Face Textures},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
month =... | Deep generative models can synthesize photorealistic images of human faces with novel identities.However, a key challenge to the wide applicability of such techniques is to provide independent control over semantically meaningful parameters: appearance, head pose, face shape, and facial expressions. In this paper, we p... |
Wang_Domain_Adaptive_Semantic_Segmentation_With_Self-Supervised_Depth_Estimation_ICCV_2021_paper | Domain Adaptive Semantic Segmentation With Self-Supervised Depth Estimation | [
"Qin Wang",
"Dengxin Dai",
"Lukas Hoyer",
"Luc Van Gool",
"Olga Fink"
] | https://openaccess.thecvf.com/content/ICCV2021/html/Wang_Domain_Adaptive_Semantic_Segmentation_With_Self-Supervised_Depth_Estimation_ICCV_2021_paper.html | https://openaccess.thecvf.com/content/ICCV2021/papers/Wang_Domain_Adaptive_Semantic_Segmentation_With_Self-Supervised_Depth_Estimation_ICCV_2021_paper.pdf | https://openaccess.thecvf.com/content/ICCV2021/supplemental/Wang_Domain_Adaptive_Semantic_ICCV_2021_supplemental.pdf | 2104.13613 | title_snapshot | @InProceedings{Wang_2021_ICCV,
author = {Wang, Qin and Dai, Dengxin and Hoyer, Lukas and Van Gool, Luc and Fink, Olga},
title = {Domain Adaptive Semantic Segmentation With Self-Supervised Depth Estimation},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
... | Domain adaptation for semantic segmentation aims to improve the model performance in the presence of a distribution shift between source and target domain. Leveraging the supervision from auxiliary tasks (such as depth estimation) has the potential to heal this shift because many visual tasks are closely related to eac... |
Gideon_The_Way_to_My_Heart_Is_Through_Contrastive_Learning_Remote_ICCV_2021_paper | The Way to My Heart Is Through Contrastive Learning: Remote Photoplethysmography From Unlabelled Video | [
"John Gideon",
"Simon Stent"
] | https://openaccess.thecvf.com/content/ICCV2021/html/Gideon_The_Way_to_My_Heart_Is_Through_Contrastive_Learning_Remote_ICCV_2021_paper.html | https://openaccess.thecvf.com/content/ICCV2021/papers/Gideon_The_Way_to_My_Heart_Is_Through_Contrastive_Learning_Remote_ICCV_2021_paper.pdf | https://openaccess.thecvf.com/content/ICCV2021/supplemental/Gideon_The_Way_to_ICCV_2021_supplemental.pdf | 2111.09748 | title_snapshot | @InProceedings{Gideon_2021_ICCV,
author = {Gideon, John and Stent, Simon},
title = {The Way to My Heart Is Through Contrastive Learning: Remote Photoplethysmography From Unlabelled Video},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
month = {O... | The ability to reliably estimate physiological signals from video is a powerful tool in low-cost, pre-clinical health monitoring. In this work we propose a new approach to remote photoplethysmography (rPPG) -- the measurement of blood volume changes from observations of a person's face or skin. Similar to current state... |
Cheng_IICNet_A_Generic_Framework_for_Reversible_Image_Conversion_ICCV_2021_paper | IICNet: A Generic Framework for Reversible Image Conversion | [
"Ka Leong Cheng",
"Yueqi Xie",
"Qifeng Chen"
] | https://openaccess.thecvf.com/content/ICCV2021/html/Cheng_IICNet_A_Generic_Framework_for_Reversible_Image_Conversion_ICCV_2021_paper.html | https://openaccess.thecvf.com/content/ICCV2021/papers/Cheng_IICNet_A_Generic_Framework_for_Reversible_Image_Conversion_ICCV_2021_paper.pdf | null | 2109.04242 | cvf | @InProceedings{Cheng_2021_ICCV,
author = {Cheng, Ka Leong and Xie, Yueqi and Chen, Qifeng},
title = {IICNet: A Generic Framework for Reversible Image Conversion},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
month = {October},
year = {... | Reversible image conversion (RIC) aims to build a reversible transformation between specific visual content (e.g., short videos) and an embedding image, where the original content can be restored from the embedding when necessary. This work develops Invertible Image Conversion Net (IICNet) as a generic solution to vari... |
Lee_Deep_Hough_Voting_for_Robust_Global_Registration_ICCV_2021_paper | Deep Hough Voting for Robust Global Registration | [
"Junha Lee",
"Seungwook Kim",
"Minsu Cho",
"Jaesik Park"
] | https://openaccess.thecvf.com/content/ICCV2021/html/Lee_Deep_Hough_Voting_for_Robust_Global_Registration_ICCV_2021_paper.html | https://openaccess.thecvf.com/content/ICCV2021/papers/Lee_Deep_Hough_Voting_for_Robust_Global_Registration_ICCV_2021_paper.pdf | null | 2109.04310 | cvf | @InProceedings{Lee_2021_ICCV,
author = {Lee, Junha and Kim, Seungwook and Cho, Minsu and Park, Jaesik},
title = {Deep Hough Voting for Robust Global Registration},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
month = {October},
year = ... | Point cloud registration is the task of estimating the rigid transformation that aligns a pair of point cloud fragments. We present an efficient and robust framework for pairwise registration of real-world 3D scans, leveraging Hough voting in the 6D transformation parameter space. First, deep geometric features are ext... |
Li_Image_Synthesis_From_Layout_With_Locality-Aware_Mask_Adaption_ICCV_2021_paper | Image Synthesis From Layout With Locality-Aware Mask Adaption | [
"Zejian Li",
"Jingyu Wu",
"Immanuel Koh",
"Yongchuan Tang",
"Lingyun Sun"
] | https://openaccess.thecvf.com/content/ICCV2021/html/Li_Image_Synthesis_From_Layout_With_Locality-Aware_Mask_Adaption_ICCV_2021_paper.html | https://openaccess.thecvf.com/content/ICCV2021/papers/Li_Image_Synthesis_From_Layout_With_Locality-Aware_Mask_Adaption_ICCV_2021_paper.pdf | https://openaccess.thecvf.com/content/ICCV2021/supplemental/Li_Image_Synthesis_From_ICCV_2021_supplemental.pdf | null | null | @InProceedings{Li_2021_ICCV,
author = {Li, Zejian and Wu, Jingyu and Koh, Immanuel and Tang, Yongchuan and Sun, Lingyun},
title = {Image Synthesis From Layout With Locality-Aware Mask Adaption},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
month ... | This paper is concerned with synthesizing images conditioned on a layout (a set of bounding boxes with object categories). Existing works construct a layout-mask-image pipeline. Object masks are generated separately and mapped to bounding boxes to form a whole semantic segmentation mask (layout-to-mask), with which a n... |
Kukleva_Generalized_and_Incremental_Few-Shot_Learning_by_Explicit_Learning_and_Calibration_ICCV_2021_paper | Generalized and Incremental Few-Shot Learning by Explicit Learning and Calibration Without Forgetting | [
"Anna Kukleva",
"Hilde Kuehne",
"Bernt Schiele"
] | https://openaccess.thecvf.com/content/ICCV2021/html/Kukleva_Generalized_and_Incremental_Few-Shot_Learning_by_Explicit_Learning_and_Calibration_ICCV_2021_paper.html | https://openaccess.thecvf.com/content/ICCV2021/papers/Kukleva_Generalized_and_Incremental_Few-Shot_Learning_by_Explicit_Learning_and_Calibration_ICCV_2021_paper.pdf | https://openaccess.thecvf.com/content/ICCV2021/supplemental/Kukleva_Generalized_and_Incremental_ICCV_2021_supplemental.pdf | 2108.08165 | cvf | @InProceedings{Kukleva_2021_ICCV,
author = {Kukleva, Anna and Kuehne, Hilde and Schiele, Bernt},
title = {Generalized and Incremental Few-Shot Learning by Explicit Learning and Calibration Without Forgetting},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)... | Both generalized and incremental few-shot learning have to deal with three major challenges: learning novel classes from only few samples per class, preventing catastrophic forgetting of base classes, and classifier calibration across novel and base classes. In this work we propose a three-stage framework that allows t... |
Pan_Scribble-Supervised_Semantic_Segmentation_by_Uncertainty_Reduction_on_Neural_Representation_and_ICCV_2021_paper | Scribble-Supervised Semantic Segmentation by Uncertainty Reduction on Neural Representation and Self-Supervision on Neural Eigenspace | [
"Zhiyi Pan",
"Peng Jiang",
"Yunhai Wang",
"Changhe Tu",
"Anthony G. Cohn"
] | https://openaccess.thecvf.com/content/ICCV2021/html/Pan_Scribble-Supervised_Semantic_Segmentation_by_Uncertainty_Reduction_on_Neural_Representation_and_ICCV_2021_paper.html | https://openaccess.thecvf.com/content/ICCV2021/papers/Pan_Scribble-Supervised_Semantic_Segmentation_by_Uncertainty_Reduction_on_Neural_Representation_and_ICCV_2021_paper.pdf | https://openaccess.thecvf.com/content/ICCV2021/supplemental/Pan_Scribble-Supervised_Semantic_Segmentation_ICCV_2021_supplemental.pdf | 2102.09896 | cvf | @InProceedings{Pan_2021_ICCV,
author = {Pan, Zhiyi and Jiang, Peng and Wang, Yunhai and Tu, Changhe and Cohn, Anthony G.},
title = {Scribble-Supervised Semantic Segmentation by Uncertainty Reduction on Neural Representation and Self-Supervision on Neural Eigenspace},
booktitle = {Proceedings of the I... | Scribble-supervised semantic segmentation has gained much attention recently for its promising performance without high-quality annotations. Due to the lack of supervision, confident and consistent predictions are usually hard to obtain. Typically, people handle these problems by either adopting an auxiliary task with ... |
Luo_Unsupervised_Domain_Adaptive_3D_Detection_With_Multi-Level_Consistency_ICCV_2021_paper | Unsupervised Domain Adaptive 3D Detection With Multi-Level Consistency | [
"Zhipeng Luo",
"Zhongang Cai",
"Changqing Zhou",
"Gongjie Zhang",
"Haiyu Zhao",
"Shuai Yi",
"Shijian Lu",
"Hongsheng Li",
"Shanghang Zhang",
"Ziwei Liu"
] | https://openaccess.thecvf.com/content/ICCV2021/html/Luo_Unsupervised_Domain_Adaptive_3D_Detection_With_Multi-Level_Consistency_ICCV_2021_paper.html | https://openaccess.thecvf.com/content/ICCV2021/papers/Luo_Unsupervised_Domain_Adaptive_3D_Detection_With_Multi-Level_Consistency_ICCV_2021_paper.pdf | https://openaccess.thecvf.com/content/ICCV2021/supplemental/Luo_Unsupervised_Domain_Adaptive_ICCV_2021_supplemental.pdf | 2107.11355 | cvf | @InProceedings{Luo_2021_ICCV,
author = {Luo, Zhipeng and Cai, Zhongang and Zhou, Changqing and Zhang, Gongjie and Zhao, Haiyu and Yi, Shuai and Lu, Shijian and Li, Hongsheng and Zhang, Shanghang and Liu, Ziwei},
title = {Unsupervised Domain Adaptive 3D Detection With Multi-Level Consistency},
booktit... | Deep learning-based 3D object detection has achieved unprecedented success with the advent of large-scale autonomous driving datasets. However, drastic performance degradation remains a critical challenge for cross-domain deployment. In addition, existing 3D domain adaptive detection methods often assume prior access t... |
Yue_Transporting_Causal_Mechanisms_for_Unsupervised_Domain_Adaptation_ICCV_2021_paper | Transporting Causal Mechanisms for Unsupervised Domain Adaptation | [
"Zhongqi Yue",
"Qianru Sun",
"Xian-Sheng Hua",
"Hanwang Zhang"
] | https://openaccess.thecvf.com/content/ICCV2021/html/Yue_Transporting_Causal_Mechanisms_for_Unsupervised_Domain_Adaptation_ICCV_2021_paper.html | https://openaccess.thecvf.com/content/ICCV2021/papers/Yue_Transporting_Causal_Mechanisms_for_Unsupervised_Domain_Adaptation_ICCV_2021_paper.pdf | https://openaccess.thecvf.com/content/ICCV2021/supplemental/Yue_Transporting_Causal_Mechanisms_ICCV_2021_supplemental.pdf | 2107.11055 | cvf | @InProceedings{Yue_2021_ICCV,
author = {Yue, Zhongqi and Sun, Qianru and Hua, Xian-Sheng and Zhang, Hanwang},
title = {Transporting Causal Mechanisms for Unsupervised Domain Adaptation},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
month = {Oct... | Existing Unsupervised Domain Adaptation (UDA) literature adopts the covariate shift and conditional shift assumptions, which essentially encourage models to learn common features across domains. However, due to the lack of supervision in the target domain, they suffer from the semantic loss: the feature will inevitably... |
Jiang_Learning_To_Estimate_Hidden_Motions_With_Global_Motion_Aggregation_ICCV_2021_paper | Learning To Estimate Hidden Motions With Global Motion Aggregation | [
"Shihao Jiang",
"Dylan Campbell",
"Yao Lu",
"Hongdong Li",
"Richard Hartley"
] | https://openaccess.thecvf.com/content/ICCV2021/html/Jiang_Learning_To_Estimate_Hidden_Motions_With_Global_Motion_Aggregation_ICCV_2021_paper.html | https://openaccess.thecvf.com/content/ICCV2021/papers/Jiang_Learning_To_Estimate_Hidden_Motions_With_Global_Motion_Aggregation_ICCV_2021_paper.pdf | https://openaccess.thecvf.com/content/ICCV2021/supplemental/Jiang_Learning_To_Estimate_ICCV_2021_supplemental.pdf | 2104.02409 | cvf | @InProceedings{Jiang_2021_ICCV,
author = {Jiang, Shihao and Campbell, Dylan and Lu, Yao and Li, Hongdong and Hartley, Richard},
title = {Learning To Estimate Hidden Motions With Global Motion Aggregation},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
... | Occlusions pose a significant challenge to optical flow algorithms that rely on local evidences. We consider an occluded point to be one that is imaged in the first frame but not in the next, a slight overloading of the standard definition since it also includes points that move out-of-frame. Estimating the motion of t... |
Guillory_Predicting_With_Confidence_on_Unseen_Distributions_ICCV_2021_paper | Predicting With Confidence on Unseen Distributions | [
"Devin Guillory",
"Vaishaal Shankar",
"Sayna Ebrahimi",
"Trevor Darrell",
"Ludwig Schmidt"
] | https://openaccess.thecvf.com/content/ICCV2021/html/Guillory_Predicting_With_Confidence_on_Unseen_Distributions_ICCV_2021_paper.html | https://openaccess.thecvf.com/content/ICCV2021/papers/Guillory_Predicting_With_Confidence_on_Unseen_Distributions_ICCV_2021_paper.pdf | https://openaccess.thecvf.com/content/ICCV2021/supplemental/Guillory_Predicting_With_Confidence_ICCV_2021_supplemental.pdf | 2107.03315 | cvf | @InProceedings{Guillory_2021_ICCV,
author = {Guillory, Devin and Shankar, Vaishaal and Ebrahimi, Sayna and Darrell, Trevor and Schmidt, Ludwig},
title = {Predicting With Confidence on Unseen Distributions},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
... | Recent work has shown that the accuracy of machine learning models can vary substantially when evaluated on a distribution that even slightly differs from that of the training data. As a result, predicting model performance on previously unseen distributions without access to labeled data is an important challenge with... |
Liu_TAM_Temporal_Adaptive_Module_for_Video_Recognition_ICCV_2021_paper | TAM: Temporal Adaptive Module for Video Recognition | [
"Zhaoyang Liu",
"Limin Wang",
"Wayne Wu",
"Chen Qian",
"Tong Lu"
] | https://openaccess.thecvf.com/content/ICCV2021/html/Liu_TAM_Temporal_Adaptive_Module_for_Video_Recognition_ICCV_2021_paper.html | https://openaccess.thecvf.com/content/ICCV2021/papers/Liu_TAM_Temporal_Adaptive_Module_for_Video_Recognition_ICCV_2021_paper.pdf | https://openaccess.thecvf.com/content/ICCV2021/supplemental/Liu_TAM_Temporal_Adaptive_ICCV_2021_supplemental.pdf | 2005.06803 | cvf | @InProceedings{Liu_2021_ICCV,
author = {Liu, Zhaoyang and Wang, Limin and Wu, Wayne and Qian, Chen and Lu, Tong},
title = {TAM: Temporal Adaptive Module for Video Recognition},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
month = {October},
... | Video data is with complex temporal dynamics due to various factors such as camera motion, speed variation, and different activities. To effectively capture this diverse motion pattern, this paper presents a new temporal adaptive module (TAM) to generate video-specific temporal kernels based on its own feature map. TAM... |
Zhao_Generating_Masks_From_Boxes_by_Mining_Spatio-Temporal_Consistencies_in_Videos_ICCV_2021_paper | Generating Masks From Boxes by Mining Spatio-Temporal Consistencies in Videos | [
"Bin Zhao",
"Goutam Bhat",
"Martin Danelljan",
"Luc Van Gool",
"Radu Timofte"
] | https://openaccess.thecvf.com/content/ICCV2021/html/Zhao_Generating_Masks_From_Boxes_by_Mining_Spatio-Temporal_Consistencies_in_Videos_ICCV_2021_paper.html | https://openaccess.thecvf.com/content/ICCV2021/papers/Zhao_Generating_Masks_From_Boxes_by_Mining_Spatio-Temporal_Consistencies_in_Videos_ICCV_2021_paper.pdf | https://openaccess.thecvf.com/content/ICCV2021/supplemental/Zhao_Generating_Masks_From_ICCV_2021_supplemental.zip | 2101.02196 | cvf | @InProceedings{Zhao_2021_ICCV,
author = {Zhao, Bin and Bhat, Goutam and Danelljan, Martin and Van Gool, Luc and Timofte, Radu},
title = {Generating Masks From Boxes by Mining Spatio-Temporal Consistencies in Videos},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision... | Segmenting objects in videos is a fundamental computer vision task. The current deep learning based paradigm offers a powerful, but data-hungry solution. However, current datasets are limited by the cost and human effort of annotating object masks in videos. This effectively limits the performance and generalization ca... |
Zhou_TRAR_Routing_the_Attention_Spans_in_Transformer_for_Visual_Question_ICCV_2021_paper | TRAR: Routing the Attention Spans in Transformer for Visual Question Answering | [
"Yiyi Zhou",
"Tianhe Ren",
"Chaoyang Zhu",
"Xiaoshuai Sun",
"Jianzhuang Liu",
"Xinghao Ding",
"Mingliang Xu",
"Rongrong Ji"
] | https://openaccess.thecvf.com/content/ICCV2021/html/Zhou_TRAR_Routing_the_Attention_Spans_in_Transformer_for_Visual_Question_ICCV_2021_paper.html | https://openaccess.thecvf.com/content/ICCV2021/papers/Zhou_TRAR_Routing_the_Attention_Spans_in_Transformer_for_Visual_Question_ICCV_2021_paper.pdf | null | null | null | @InProceedings{Zhou_2021_ICCV,
author = {Zhou, Yiyi and Ren, Tianhe and Zhu, Chaoyang and Sun, Xiaoshuai and Liu, Jianzhuang and Ding, Xinghao and Xu, Mingliang and Ji, Rongrong},
title = {TRAR: Routing the Attention Spans in Transformer for Visual Question Answering},
booktitle = {Proceedings of the... | Due to the superior ability of global dependency modeling, Transformer and its variants have become the primary choice of many vision-and-language tasks. However, in tasks like Visual Question Answering (VQA) and Referring Expression Comprehension (REC), the multimodal prediction often requires visual information from ... |
Melnyk_Embed_Me_if_You_Can_A_Geometric_Perceptron_ICCV_2021_paper | Embed Me if You Can: A Geometric Perceptron | [
"Pavlo Melnyk",
"Michael Felsberg",
"Mårten Wadenbäck"
] | https://openaccess.thecvf.com/content/ICCV2021/html/Melnyk_Embed_Me_if_You_Can_A_Geometric_Perceptron_ICCV_2021_paper.html | https://openaccess.thecvf.com/content/ICCV2021/papers/Melnyk_Embed_Me_if_You_Can_A_Geometric_Perceptron_ICCV_2021_paper.pdf | https://openaccess.thecvf.com/content/ICCV2021/supplemental/Melnyk_Embed_Me_if_ICCV_2021_supplemental.zip | 2006.06507 | title_snapshot | @InProceedings{Melnyk_2021_ICCV,
author = {Melnyk, Pavlo and Felsberg, Michael and Wadenb\"ack, M\r{a}rten},
title = {Embed Me if You Can: A Geometric Perceptron},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
month = {October},
year = ... | Solving geometric tasks involving point clouds by using machine learning is a challenging problem. Standard feed-forward neural networks combine linear or, if the bias parameter is included, affine layers and activation functions. Their geometric modeling is limited, which motivated the prior work introducing the multi... |
Mullapudi_Learning_Rare_Category_Classifiers_on_a_Tight_Labeling_Budget_ICCV_2021_paper | Learning Rare Category Classifiers on a Tight Labeling Budget | [
"Ravi Teja Mullapudi",
"Fait Poms",
"William R. Mark",
"Deva Ramanan",
"Kayvon Fatahalian"
] | https://openaccess.thecvf.com/content/ICCV2021/html/Mullapudi_Learning_Rare_Category_Classifiers_on_a_Tight_Labeling_Budget_ICCV_2021_paper.html | https://openaccess.thecvf.com/content/ICCV2021/papers/Mullapudi_Learning_Rare_Category_Classifiers_on_a_Tight_Labeling_Budget_ICCV_2021_paper.pdf | https://openaccess.thecvf.com/content/ICCV2021/supplemental/Mullapudi_Learning_Rare_Category_ICCV_2021_supplemental.pdf | null | null | @InProceedings{Mullapudi_2021_ICCV,
author = {Mullapudi, Ravi Teja and Poms, Fait and Mark, William R. and Ramanan, Deva and Fatahalian, Kayvon},
title = {Learning Rare Category Classifiers on a Tight Labeling Budget},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Visi... | Many real-world ML deployments face the challenge of training a rare category model with a small labeling bud- get. In these settings, there is often access to large amounts of unlabeled data, therefore it is attractive to consider semi-supervised or active learning approaches to reduce human labeling effort. However, ... |
Wong_Persistent_Homology_Based_Graph_Convolution_Network_for_Fine-Grained_3D_Shape_ICCV_2021_paper | Persistent Homology Based Graph Convolution Network for Fine-Grained 3D Shape Segmentation | [
"Chi-Chong Wong",
"Chi-Man Vong"
] | https://openaccess.thecvf.com/content/ICCV2021/html/Wong_Persistent_Homology_Based_Graph_Convolution_Network_for_Fine-Grained_3D_Shape_ICCV_2021_paper.html | https://openaccess.thecvf.com/content/ICCV2021/papers/Wong_Persistent_Homology_Based_Graph_Convolution_Network_for_Fine-Grained_3D_Shape_ICCV_2021_paper.pdf | null | null | null | @InProceedings{Wong_2021_ICCV,
author = {Wong, Chi-Chong and Vong, Chi-Man},
title = {Persistent Homology Based Graph Convolution Network for Fine-Grained 3D Shape Segmentation},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
month = {October},
... | Fine-grained 3D segmentation is an important task in 3D object understanding, especially in applications such as intelligent manufacturing or parts analysis for 3D objects. However, many challenges involved in such problem are yet to be solved, such as i) interpreting the complex structures located in different regions... |
Liu_Hybrid_Neural_Fusion_for_Full-Frame_Video_Stabilization_ICCV_2021_paper | Hybrid Neural Fusion for Full-Frame Video Stabilization | [
"Yu-Lun Liu",
"Wei-Sheng Lai",
"Ming-Hsuan Yang",
"Yung-Yu Chuang",
"Jia-Bin Huang"
] | https://openaccess.thecvf.com/content/ICCV2021/html/Liu_Hybrid_Neural_Fusion_for_Full-Frame_Video_Stabilization_ICCV_2021_paper.html | https://openaccess.thecvf.com/content/ICCV2021/papers/Liu_Hybrid_Neural_Fusion_for_Full-Frame_Video_Stabilization_ICCV_2021_paper.pdf | https://openaccess.thecvf.com/content/ICCV2021/supplemental/Liu_Hybrid_Neural_Fusion_ICCV_2021_supplemental.pdf | 2102.06205 | cvf | @InProceedings{Liu_2021_ICCV,
author = {Liu, Yu-Lun and Lai, Wei-Sheng and Yang, Ming-Hsuan and Chuang, Yung-Yu and Huang, Jia-Bin},
title = {Hybrid Neural Fusion for Full-Frame Video Stabilization},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
mon... | Existing video stabilization methods often generate visible distortion or require aggressive cropping of frame boundaries, resulting in smaller field of views. In this work, we present a frame synthesis algorithm to achieve full-frame video stabilization. We first estimate dense warp fields from neighboring frames and ... |
Kundu_HIRE-SNN_Harnessing_the_Inherent_Robustness_of_Energy-Efficient_Deep_Spiking_Neural_ICCV_2021_paper | HIRE-SNN: Harnessing the Inherent Robustness of Energy-Efficient Deep Spiking Neural Networks by Training With Crafted Input Noise | [
"Souvik Kundu",
"Massoud Pedram",
"Peter A. Beerel"
] | https://openaccess.thecvf.com/content/ICCV2021/html/Kundu_HIRE-SNN_Harnessing_the_Inherent_Robustness_of_Energy-Efficient_Deep_Spiking_Neural_ICCV_2021_paper.html | https://openaccess.thecvf.com/content/ICCV2021/papers/Kundu_HIRE-SNN_Harnessing_the_Inherent_Robustness_of_Energy-Efficient_Deep_Spiking_Neural_ICCV_2021_paper.pdf | https://openaccess.thecvf.com/content/ICCV2021/supplemental/Kundu_HIRE-SNN_Harnessing_the_ICCV_2021_supplemental.pdf | 2110.11417 | title_snapshot | @InProceedings{Kundu_2021_ICCV,
author = {Kundu, Souvik and Pedram, Massoud and Beerel, Peter A.},
title = {HIRE-SNN: Harnessing the Inherent Robustness of Energy-Efficient Deep Spiking Neural Networks by Training With Crafted Input Noise},
booktitle = {Proceedings of the IEEE/CVF International Confe... | Low-latency deep spiking neural networks (SNNs) have become a promising alternative to conventional artificial neural networks (ANNs) because of their potential for increased energy efficiency on event-driven neuromorphic hardware. Neural networks, including SNNs, however, are subject to various adversarial attacks and... |
He_CDNet_Centripetal_Direction_Network_for_Nuclear_Instance_Segmentation_ICCV_2021_paper | CDNet: Centripetal Direction Network for Nuclear Instance Segmentation | [
"Hongliang He",
"Zhongyi Huang",
"Yao Ding",
"Guoli Song",
"Lin Wang",
"Qian Ren",
"Pengxu Wei",
"Zhiqiang Gao",
"Jie Chen"
] | https://openaccess.thecvf.com/content/ICCV2021/html/He_CDNet_Centripetal_Direction_Network_for_Nuclear_Instance_Segmentation_ICCV_2021_paper.html | https://openaccess.thecvf.com/content/ICCV2021/papers/He_CDNet_Centripetal_Direction_Network_for_Nuclear_Instance_Segmentation_ICCV_2021_paper.pdf | null | null | null | @InProceedings{He_2021_ICCV,
author = {He, Hongliang and Huang, Zhongyi and Ding, Yao and Song, Guoli and Wang, Lin and Ren, Qian and Wei, Pengxu and Gao, Zhiqiang and Chen, Jie},
title = {CDNet: Centripetal Direction Network for Nuclear Instance Segmentation},
booktitle = {Proceedings of the IEEE/CV... | Nuclear instance segmentation is a challenging task due to a large number of touching and overlapping nuclei in pathological images. Existing methods cannot effectively recognize the accurate boundary owing to neglecting the relationship between pixels (e.g., direction information). In this paper, we propose a novel Ce... |
Xu_3D_Human_Texture_Estimation_From_a_Single_Image_With_Transformers_ICCV_2021_paper | 3D Human Texture Estimation From a Single Image With Transformers | [
"Xiangyu Xu",
"Chen Change Loy"
] | https://openaccess.thecvf.com/content/ICCV2021/html/Xu_3D_Human_Texture_Estimation_From_a_Single_Image_With_Transformers_ICCV_2021_paper.html | https://openaccess.thecvf.com/content/ICCV2021/papers/Xu_3D_Human_Texture_Estimation_From_a_Single_Image_With_Transformers_ICCV_2021_paper.pdf | null | 2109.02563 | cvf | @InProceedings{Xu_2021_ICCV,
author = {Xu, Xiangyu and Loy, Chen Change},
title = {3D Human Texture Estimation From a Single Image With Transformers},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
month = {October},
year = {2021},
p... | We propose a Transformer-based framework for 3D human texture estimation from a single image. The proposed Transformer is able to effectively exploit the global information of the input image, overcoming the limitations of existing methods that are solely based on convolutional neural networks. In addition, we also pro... |
Zhao_The_Surprising_Effectiveness_of_Visual_Odometry_Techniques_for_Embodied_PointGoal_ICCV_2021_paper | The Surprising Effectiveness of Visual Odometry Techniques for Embodied PointGoal Navigation | [
"Xiaoming Zhao",
"Harsh Agrawal",
"Dhruv Batra",
"Alexander G. Schwing"
] | https://openaccess.thecvf.com/content/ICCV2021/html/Zhao_The_Surprising_Effectiveness_of_Visual_Odometry_Techniques_for_Embodied_PointGoal_ICCV_2021_paper.html | https://openaccess.thecvf.com/content/ICCV2021/papers/Zhao_The_Surprising_Effectiveness_of_Visual_Odometry_Techniques_for_Embodied_PointGoal_ICCV_2021_paper.pdf | https://openaccess.thecvf.com/content/ICCV2021/supplemental/Zhao_The_Surprising_Effectiveness_ICCV_2021_supplemental.pdf | 2108.11550 | cvf | @InProceedings{Zhao_2021_ICCV,
author = {Zhao, Xiaoming and Agrawal, Harsh and Batra, Dhruv and Schwing, Alexander G.},
title = {The Surprising Effectiveness of Visual Odometry Techniques for Embodied PointGoal Navigation},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer... | It is fundamental for personal robots to reliably navigate to a specified goal. To study this task, PointGoal navigation has been introduced in simulated Embodied AI environments. Recent advances solve this PointGoal navigation task with near-perfect accuracy (99.6% success) in photo-realistically simulated environment... |
Verwimp_Rehearsal_Revealed_The_Limits_and_Merits_of_Revisiting_Samples_in_ICCV_2021_paper | Rehearsal Revealed: The Limits and Merits of Revisiting Samples in Continual Learning | [
"Eli Verwimp",
"Matthias De Lange",
"Tinne Tuytelaars"
] | https://openaccess.thecvf.com/content/ICCV2021/html/Verwimp_Rehearsal_Revealed_The_Limits_and_Merits_of_Revisiting_Samples_in_ICCV_2021_paper.html | https://openaccess.thecvf.com/content/ICCV2021/papers/Verwimp_Rehearsal_Revealed_The_Limits_and_Merits_of_Revisiting_Samples_in_ICCV_2021_paper.pdf | https://openaccess.thecvf.com/content/ICCV2021/supplemental/Verwimp_Rehearsal_Revealed_The_ICCV_2021_supplemental.pdf | 2104.07446 | cvf | @InProceedings{Verwimp_2021_ICCV,
author = {Verwimp, Eli and De Lange, Matthias and Tuytelaars, Tinne},
title = {Rehearsal Revealed: The Limits and Merits of Revisiting Samples in Continual Learning},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
mo... | Learning from non-stationary data streams and overcoming catastrophic forgetting still poses a serious challenge for machine learning research. Rather than aiming to improve state-of-the-art, in this work we provide insight into the limits and merits of rehearsal, one of continual learning's most established methods. W... |
Liu_Group-Free_3D_Object_Detection_via_Transformers_ICCV_2021_paper | Group-Free 3D Object Detection via Transformers | [
"Ze Liu",
"Zheng Zhang",
"Yue Cao",
"Han Hu",
"Xin Tong"
] | https://openaccess.thecvf.com/content/ICCV2021/html/Liu_Group-Free_3D_Object_Detection_via_Transformers_ICCV_2021_paper.html | https://openaccess.thecvf.com/content/ICCV2021/papers/Liu_Group-Free_3D_Object_Detection_via_Transformers_ICCV_2021_paper.pdf | https://openaccess.thecvf.com/content/ICCV2021/supplemental/Liu_Group-Free_3D_Object_ICCV_2021_supplemental.pdf | 2104.00678 | cvf | @InProceedings{Liu_2021_ICCV,
author = {Liu, Ze and Zhang, Zheng and Cao, Yue and Hu, Han and Tong, Xin},
title = {Group-Free 3D Object Detection via Transformers},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
month = {October},
year =... | Recently, directly detecting 3D objects from 3D point clouds has received increasing attention. To extract object representation from an irregular point cloud, existing methods usually take a point grouping step to assign the points to an object candidate so that a PointNet-like network could be used to derive object f... |
Li_Discover_the_Unknown_Biased_Attribute_of_an_Image_Classifier_ICCV_2021_paper | Discover the Unknown Biased Attribute of an Image Classifier | [
"Zhiheng Li",
"Chenliang Xu"
] | https://openaccess.thecvf.com/content/ICCV2021/html/Li_Discover_the_Unknown_Biased_Attribute_of_an_Image_Classifier_ICCV_2021_paper.html | https://openaccess.thecvf.com/content/ICCV2021/papers/Li_Discover_the_Unknown_Biased_Attribute_of_an_Image_Classifier_ICCV_2021_paper.pdf | https://openaccess.thecvf.com/content/ICCV2021/supplemental/Li_Discover_the_Unknown_ICCV_2021_supplemental.pdf | 2104.14556 | cvf | @InProceedings{Li_2021_ICCV,
author = {Li, Zhiheng and Xu, Chenliang},
title = {Discover the Unknown Biased Attribute of an Image Classifier},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
month = {October},
year = {2021},
pages ... | Recent works find that AI algorithms learn biases from data. Therefore, it is urgent and vital to identify biases in AI algorithms. However, the previous bias identification pipeline overly relies on human experts to conjecture potential biases (e.g., gender), which may neglect other underlying biases not realized by h... |
Liu_Learn_To_Cluster_Faces_via_Pairwise_Classification_ICCV_2021_paper | Learn To Cluster Faces via Pairwise Classification | [
"Junfu Liu",
"Di Qiu",
"Pengfei Yan",
"Xiaolin Wei"
] | https://openaccess.thecvf.com/content/ICCV2021/html/Liu_Learn_To_Cluster_Faces_via_Pairwise_Classification_ICCV_2021_paper.html | https://openaccess.thecvf.com/content/ICCV2021/papers/Liu_Learn_To_Cluster_Faces_via_Pairwise_Classification_ICCV_2021_paper.pdf | null | 2205.13117 | title_snapshot | @InProceedings{Liu_2021_ICCV,
author = {Liu, Junfu and Qiu, Di and Yan, Pengfei and Wei, Xiaolin},
title = {Learn To Cluster Faces via Pairwise Classification},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
month = {October},
year = {20... | Face clustering plays an essential role in exploiting massive unlabeled face data. Recently, graph-based face clustering methods are getting popular for their satisfying performances. However, they usually suffer from excessive memory consumption especially on large-scale graphs, and rely on empirical thresholds to det... |
Ruan_DAE-GAN_Dynamic_Aspect-Aware_GAN_for_Text-to-Image_Synthesis_ICCV_2021_paper | DAE-GAN: Dynamic Aspect-Aware GAN for Text-to-Image Synthesis | [
"Shulan Ruan",
"Yong Zhang",
"Kun Zhang",
"Yanbo Fan",
"Fan Tang",
"Qi Liu",
"Enhong Chen"
] | https://openaccess.thecvf.com/content/ICCV2021/html/Ruan_DAE-GAN_Dynamic_Aspect-Aware_GAN_for_Text-to-Image_Synthesis_ICCV_2021_paper.html | https://openaccess.thecvf.com/content/ICCV2021/papers/Ruan_DAE-GAN_Dynamic_Aspect-Aware_GAN_for_Text-to-Image_Synthesis_ICCV_2021_paper.pdf | https://openaccess.thecvf.com/content/ICCV2021/supplemental/Ruan_DAE-GAN_Dynamic_Aspect-Aware_ICCV_2021_supplemental.pdf | 2108.12141 | title_snapshot | @InProceedings{Ruan_2021_ICCV,
author = {Ruan, Shulan and Zhang, Yong and Zhang, Kun and Fan, Yanbo and Tang, Fan and Liu, Qi and Chen, Enhong},
title = {DAE-GAN: Dynamic Aspect-Aware GAN for Text-to-Image Synthesis},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Visio... | Text-to-image synthesis refers to generating an image from a given text description, the key goal of which lies in photo realism and semantic consistency. Previous methods usually generate an initial image with sentence embedding and then refine it with fine-grained word embedding. Despite the significant progress, the... |
Chang_Learning_Facial_Representations_From_the_Cycle-Consistency_of_Face_ICCV_2021_paper | Learning Facial Representations From the Cycle-Consistency of Face | [
"Jia-Ren Chang",
"Yong-Sheng Chen",
"Wei-Chen Chiu"
] | https://openaccess.thecvf.com/content/ICCV2021/html/Chang_Learning_Facial_Representations_From_the_Cycle-Consistency_of_Face_ICCV_2021_paper.html | https://openaccess.thecvf.com/content/ICCV2021/papers/Chang_Learning_Facial_Representations_From_the_Cycle-Consistency_of_Face_ICCV_2021_paper.pdf | https://openaccess.thecvf.com/content/ICCV2021/supplemental/Chang_Learning_Facial_Representations_ICCV_2021_supplemental.pdf | 2108.03427 | cvf | @InProceedings{Chang_2021_ICCV,
author = {Chang, Jia-Ren and Chen, Yong-Sheng and Chiu, Wei-Chen},
title = {Learning Facial Representations From the Cycle-Consistency of Face},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
month = {October},
... | Faces manifest large variations in many aspects, such as identity, expression, pose, and face styling. Therefore, it is a great challenge to disentangle and extract these characteristics from facial images, especially in an unsupervised manner. In this work, we introduce cycle-consistency in facial characteristics as f... |
Gu_Towards_Memory-Efficient_Neural_Networks_via_Multi-Level_In_Situ_Generation_ICCV_2021_paper | Towards Memory-Efficient Neural Networks via Multi-Level In Situ Generation | [
"Jiaqi Gu",
"Hanqing Zhu",
"Chenghao Feng",
"Mingjie Liu",
"Zixuan Jiang",
"Ray T. Chen",
"David Z. Pan"
] | https://openaccess.thecvf.com/content/ICCV2021/html/Gu_Towards_Memory-Efficient_Neural_Networks_via_Multi-Level_In_Situ_Generation_ICCV_2021_paper.html | https://openaccess.thecvf.com/content/ICCV2021/papers/Gu_Towards_Memory-Efficient_Neural_Networks_via_Multi-Level_In_Situ_Generation_ICCV_2021_paper.pdf | null | 2108.11430 | cvf | @InProceedings{Gu_2021_ICCV,
author = {Gu, Jiaqi and Zhu, Hanqing and Feng, Chenghao and Liu, Mingjie and Jiang, Zixuan and Chen, Ray T. and Pan, David Z.},
title = {Towards Memory-Efficient Neural Networks via Multi-Level In Situ Generation},
booktitle = {Proceedings of the IEEE/CVF International Co... | Deep neural networks (DNN) have shown superior performance in a variety of tasks. As they rapidly evolve, their escalating computation and memory demands make it challenging to deploy them on resource-constrained edge devices. Though extensive efficient accelerator designs, from traditional electronics to emerging phot... |
Han_Greedy_Gradient_Ensemble_for_Robust_Visual_Question_Answering_ICCV_2021_paper | Greedy Gradient Ensemble for Robust Visual Question Answering | [
"Xinzhe Han",
"Shuhui Wang",
"Chi Su",
"Qingming Huang",
"Qi Tian"
] | https://openaccess.thecvf.com/content/ICCV2021/html/Han_Greedy_Gradient_Ensemble_for_Robust_Visual_Question_Answering_ICCV_2021_paper.html | https://openaccess.thecvf.com/content/ICCV2021/papers/Han_Greedy_Gradient_Ensemble_for_Robust_Visual_Question_Answering_ICCV_2021_paper.pdf | https://openaccess.thecvf.com/content/ICCV2021/supplemental/Han_Greedy_Gradient_Ensemble_ICCV_2021_supplemental.pdf | 2107.12651 | cvf | @InProceedings{Han_2021_ICCV,
author = {Han, Xinzhe and Wang, Shuhui and Su, Chi and Huang, Qingming and Tian, Qi},
title = {Greedy Gradient Ensemble for Robust Visual Question Answering},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
month = {O... | Language bias is a critical issue in Visual Question Answering (VQA), where models often exploit dataset biases for the final decision without considering the image information. As a result, they suffer from performance drop on out-of-distribution data and inadequate visual explanation. Based on experimental analysis f... |
Liu_Influence_Selection_for_Active_Learning_ICCV_2021_paper | Influence Selection for Active Learning | [
"Zhuoming Liu",
"Hao Ding",
"Huaping Zhong",
"Weijia Li",
"Jifeng Dai",
"Conghui He"
] | https://openaccess.thecvf.com/content/ICCV2021/html/Liu_Influence_Selection_for_Active_Learning_ICCV_2021_paper.html | https://openaccess.thecvf.com/content/ICCV2021/papers/Liu_Influence_Selection_for_Active_Learning_ICCV_2021_paper.pdf | https://openaccess.thecvf.com/content/ICCV2021/supplemental/Liu_Influence_Selection_for_ICCV_2021_supplemental.pdf | 2108.09331 | cvf | @InProceedings{Liu_2021_ICCV,
author = {Liu, Zhuoming and Ding, Hao and Zhong, Huaping and Li, Weijia and Dai, Jifeng and He, Conghui},
title = {Influence Selection for Active Learning},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
month = {Oct... | The existing active learning methods select the samples by evaluating the sample's uncertainty or its effect on the diversity of labeled datasets based on different task-specific or model-specific criteria. In this paper, we propose the Influence Selection for Active Learning(ISAL) which selects the unlabeled samples t... |
Min_Visual_Alignment_Constraint_for_Continuous_Sign_Language_Recognition_ICCV_2021_paper | Visual Alignment Constraint for Continuous Sign Language Recognition | [
"Yuecong Min",
"Aiming Hao",
"Xiujuan Chai",
"Xilin Chen"
] | https://openaccess.thecvf.com/content/ICCV2021/html/Min_Visual_Alignment_Constraint_for_Continuous_Sign_Language_Recognition_ICCV_2021_paper.html | https://openaccess.thecvf.com/content/ICCV2021/papers/Min_Visual_Alignment_Constraint_for_Continuous_Sign_Language_Recognition_ICCV_2021_paper.pdf | https://openaccess.thecvf.com/content/ICCV2021/supplemental/Min_Visual_Alignment_Constraint_ICCV_2021_supplemental.zip | 2104.02330 | cvf | @InProceedings{Min_2021_ICCV,
author = {Min, Yuecong and Hao, Aiming and Chai, Xiujuan and Chen, Xilin},
title = {Visual Alignment Constraint for Continuous Sign Language Recognition},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
month = {Octob... | Vision-based Continuous Sign Language Recognition (CSLR) aims to recognize unsegmented signs from image streams. Overfitting is one of the most critical problems in CSLR training, and previous works show that the iterative training scheme can partially solve this problem while also costing more training time. In this s... |
Engin_On_the_Hidden_Treasure_of_Dialog_in_Video_Question_Answering_ICCV_2021_paper | On the Hidden Treasure of Dialog in Video Question Answering | [
"Deniz Engin",
"François Schnitzler",
"Ngoc Q. K. Duong",
"Yannis Avrithis"
] | https://openaccess.thecvf.com/content/ICCV2021/html/Engin_On_the_Hidden_Treasure_of_Dialog_in_Video_Question_Answering_ICCV_2021_paper.html | https://openaccess.thecvf.com/content/ICCV2021/papers/Engin_On_the_Hidden_Treasure_of_Dialog_in_Video_Question_Answering_ICCV_2021_paper.pdf | https://openaccess.thecvf.com/content/ICCV2021/supplemental/Engin_On_the_Hidden_ICCV_2021_supplemental.pdf | 2103.14517 | cvf | @InProceedings{Engin_2021_ICCV,
author = {Engin, Deniz and Schnitzler, Fran\c{c}ois and Duong, Ngoc Q. K. and Avrithis, Yannis},
title = {On the Hidden Treasure of Dialog in Video Question Answering},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
mo... | High-level understanding of stories in video such as movies and TV shows from raw data is extremely challenging. Modern video question answering (VideoQA) systems often use additional human-made sources like plot synopses, scripts, video descriptions or knowledge bases. In this work, we present a new approach to unders... |
Hsiao_From_Culture_to_Clothing_Discovering_the_World_Events_Behind_a_ICCV_2021_paper | From Culture to Clothing: Discovering the World Events Behind a Century of Fashion Images | [
"Wei-Lin Hsiao",
"Kristen Grauman"
] | https://openaccess.thecvf.com/content/ICCV2021/html/Hsiao_From_Culture_to_Clothing_Discovering_the_World_Events_Behind_a_ICCV_2021_paper.html | https://openaccess.thecvf.com/content/ICCV2021/papers/Hsiao_From_Culture_to_Clothing_Discovering_the_World_Events_Behind_a_ICCV_2021_paper.pdf | https://openaccess.thecvf.com/content/ICCV2021/supplemental/Hsiao_From_Culture_to_ICCV_2021_supplemental.pdf | 2102.01690 | cvf | @InProceedings{Hsiao_2021_ICCV,
author = {Hsiao, Wei-Lin and Grauman, Kristen},
title = {From Culture to Clothing: Discovering the World Events Behind a Century of Fashion Images},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
month = {October},... | Fashion is intertwined with external cultural factors, but identifying these links remains a manual process limited to only the most salient phenomena. We propose a data-driven approach to identify specific cultural factors affecting the clothes people wear. Using large-scale datasets of news articles and vintage photo... |
Aliakbarian_Contextually_Plausible_and_Diverse_3D_Human_Motion_Prediction_ICCV_2021_paper | Contextually Plausible and Diverse 3D Human Motion Prediction | [
"Sadegh Aliakbarian",
"Fatemeh Saleh",
"Lars Petersson",
"Stephen Gould",
"Mathieu Salzmann"
] | https://openaccess.thecvf.com/content/ICCV2021/html/Aliakbarian_Contextually_Plausible_and_Diverse_3D_Human_Motion_Prediction_ICCV_2021_paper.html | https://openaccess.thecvf.com/content/ICCV2021/papers/Aliakbarian_Contextually_Plausible_and_Diverse_3D_Human_Motion_Prediction_ICCV_2021_paper.pdf | https://openaccess.thecvf.com/content/ICCV2021/supplemental/Aliakbarian_Contextually_Plausible_and_ICCV_2021_supplemental.pdf | 1912.08521 | cvf | @InProceedings{Aliakbarian_2021_ICCV,
author = {Aliakbarian, Sadegh and Saleh, Fatemeh and Petersson, Lars and Gould, Stephen and Salzmann, Mathieu},
title = {Contextually Plausible and Diverse 3D Human Motion Prediction},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer ... | We tackle the task of diverse 3D human motion prediction, that is, forecasting multiple plausible future 3D poses given a sequence of observed 3D poses. In this context, a popular approach consists of using a Conditional Variational Autoencoder (CVAE). However, existing approaches that do so either fail to capture the ... |
Chen_BN-NAS_Neural_Architecture_Search_With_Batch_Normalization_ICCV_2021_paper | BN-NAS: Neural Architecture Search With Batch Normalization | [
"Boyu Chen",
"Peixia Li",
"Baopu Li",
"Chen Lin",
"Chuming Li",
"Ming Sun",
"Junjie Yan",
"Wanli Ouyang"
] | https://openaccess.thecvf.com/content/ICCV2021/html/Chen_BN-NAS_Neural_Architecture_Search_With_Batch_Normalization_ICCV_2021_paper.html | https://openaccess.thecvf.com/content/ICCV2021/papers/Chen_BN-NAS_Neural_Architecture_Search_With_Batch_Normalization_ICCV_2021_paper.pdf | null | 2108.07375 | title_snapshot | @InProceedings{Chen_2021_ICCV,
author = {Chen, Boyu and Li, Peixia and Li, Baopu and Lin, Chen and Li, Chuming and Sun, Ming and Yan, Junjie and Ouyang, Wanli},
title = {BN-NAS: Neural Architecture Search With Batch Normalization},
booktitle = {Proceedings of the IEEE/CVF International Conference on ... | Model training and evaluation are two main time-consuming processes during neural architecture search (NAS). Although weight-sharing based methods have been proposed to reduce the number of trained networks, these methods still need to train the supernet for hundreds of epochs and evaluate thousands of subnets to find ... |
Qiu_Condensing_a_Sequence_to_One_Informative_Frame_for_Video_Recognition_ICCV_2021_paper | Condensing a Sequence to One Informative Frame for Video Recognition | [
"Zhaofan Qiu",
"Ting Yao",
"Yan Shu",
"Chong-Wah Ngo",
"Tao Mei"
] | https://openaccess.thecvf.com/content/ICCV2021/html/Qiu_Condensing_a_Sequence_to_One_Informative_Frame_for_Video_Recognition_ICCV_2021_paper.html | https://openaccess.thecvf.com/content/ICCV2021/papers/Qiu_Condensing_a_Sequence_to_One_Informative_Frame_for_Video_Recognition_ICCV_2021_paper.pdf | null | 2201.04022 | title_snapshot | @InProceedings{Qiu_2021_ICCV,
author = {Qiu, Zhaofan and Yao, Ting and Shu, Yan and Ngo, Chong-Wah and Mei, Tao},
title = {Condensing a Sequence to One Informative Frame for Video Recognition},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
month ... | Video is complex due to large variations in motion and rich content in fine-grained visual details. Abstracting useful information from such information-intensive media requires exhaustive computing resources. This paper studies a two-step alternative that first condenses the video sequence to an informative "frame" an... |
Nowara_The_Benefit_of_Distraction_Denoising_Camera-Based_Physiological_Measurements_Using_Inverse_ICCV_2021_paper | The Benefit of Distraction: Denoising Camera-Based Physiological Measurements Using Inverse Attention | [
"Ewa M. Nowara",
"Daniel McDuff",
"Ashok Veeraraghavan"
] | https://openaccess.thecvf.com/content/ICCV2021/html/Nowara_The_Benefit_of_Distraction_Denoising_Camera-Based_Physiological_Measurements_Using_Inverse_ICCV_2021_paper.html | https://openaccess.thecvf.com/content/ICCV2021/papers/Nowara_The_Benefit_of_Distraction_Denoising_Camera-Based_Physiological_Measurements_Using_Inverse_ICCV_2021_paper.pdf | https://openaccess.thecvf.com/content/ICCV2021/supplemental/Nowara_The_Benefit_of_ICCV_2021_supplemental.pdf | 2010.07770 | title_judge | @InProceedings{Nowara_2021_ICCV,
author = {Nowara, Ewa M. and McDuff, Daniel and Veeraraghavan, Ashok},
title = {The Benefit of Distraction: Denoising Camera-Based Physiological Measurements Using Inverse Attention},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision... | Attention networks perform well on diverse computer vision tasks. The core idea is that the signal of interest is stronger in some pixels ("foreground"), and by selectively focusing computation on these pixels, networks can extract subtle information buried in noise and other sources of corruption. Our paper is based o... |
Wu_Collaborative_and_Adversarial_Learning_of_Focused_and_Dispersive_Representations_for_ICCV_2021_paper | Collaborative and Adversarial Learning of Focused and Dispersive Representations for Semi-Supervised Polyp Segmentation | [
"Huisi Wu",
"Guilian Chen",
"Zhenkun Wen",
"Jing Qin"
] | https://openaccess.thecvf.com/content/ICCV2021/html/Wu_Collaborative_and_Adversarial_Learning_of_Focused_and_Dispersive_Representations_for_ICCV_2021_paper.html | https://openaccess.thecvf.com/content/ICCV2021/papers/Wu_Collaborative_and_Adversarial_Learning_of_Focused_and_Dispersive_Representations_for_ICCV_2021_paper.pdf | https://openaccess.thecvf.com/content/ICCV2021/supplemental/Wu_Collaborative_and_Adversarial_ICCV_2021_supplemental.pdf | null | null | @InProceedings{Wu_2021_ICCV,
author = {Wu, Huisi and Chen, Guilian and Wen, Zhenkun and Qin, Jing},
title = {Collaborative and Adversarial Learning of Focused and Dispersive Representations for Semi-Supervised Polyp Segmentation},
booktitle = {Proceedings of the IEEE/CVF International Conference on C... | Automatic polyp segmentation from colonoscopy images is an essential step in computer aided diagnosis for colorectal cancer. Most of polyp segmentation methods reported in recent years are based on fully supervised deep learning. However, annotation for polyp images by physicians during the diagnosis is time-consuming ... |
Prabhu_Active_Domain_Adaptation_via_Clustering_Uncertainty-Weighted_Embeddings_ICCV_2021_paper | Active Domain Adaptation via Clustering Uncertainty-Weighted Embeddings | [
"Viraj Prabhu",
"Arjun Chandrasekaran",
"Kate Saenko",
"Judy Hoffman"
] | https://openaccess.thecvf.com/content/ICCV2021/html/Prabhu_Active_Domain_Adaptation_via_Clustering_Uncertainty-Weighted_Embeddings_ICCV_2021_paper.html | https://openaccess.thecvf.com/content/ICCV2021/papers/Prabhu_Active_Domain_Adaptation_via_Clustering_Uncertainty-Weighted_Embeddings_ICCV_2021_paper.pdf | https://openaccess.thecvf.com/content/ICCV2021/supplemental/Prabhu_Active_Domain_Adaptation_ICCV_2021_supplemental.pdf | 2010.08666 | cvf | @InProceedings{Prabhu_2021_ICCV,
author = {Prabhu, Viraj and Chandrasekaran, Arjun and Saenko, Kate and Hoffman, Judy},
title = {Active Domain Adaptation via Clustering Uncertainty-Weighted Embeddings},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
... | Generalizing deep neural networks to new target domains is critical to their real-world utility. In practice, it may be feasible to get some target data labeled, but to be cost-effective it is desirable to select a maximally-informative subset via active learning (AL). We study the problem of AL under a domain shift, c... |
Gadde_Detail_Me_More_Improving_GANs_Photo-Realism_of_Complex_Scenes_ICCV_2021_paper | Detail Me More: Improving GAN's Photo-Realism of Complex Scenes | [
"Raghudeep Gadde",
"Qianli Feng",
"Aleix M. Martinez"
] | https://openaccess.thecvf.com/content/ICCV2021/html/Gadde_Detail_Me_More_Improving_GANs_Photo-Realism_of_Complex_Scenes_ICCV_2021_paper.html | https://openaccess.thecvf.com/content/ICCV2021/papers/Gadde_Detail_Me_More_Improving_GANs_Photo-Realism_of_Complex_Scenes_ICCV_2021_paper.pdf | https://openaccess.thecvf.com/content/ICCV2021/supplemental/Gadde_Detail_Me_More_ICCV_2021_supplemental.pdf | null | null | @InProceedings{Gadde_2021_ICCV,
author = {Gadde, Raghudeep and Feng, Qianli and Martinez, Aleix M.},
title = {Detail Me More: Improving GAN's Photo-Realism of Complex Scenes},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
month = {October},
... | Generative models can synthesize photo-realistic images of a single object. For example, for human faces, algorithms learn to model the local shape and shading of the face components, i.e., changes in the brows, eyes, nose, mouth, jaw line, etc. This is possible because all faces have two brows, two eyes, a nose and a ... |
Xu_Rethinking_Self-Supervised_Correspondence_Learning_A_Video_Frame-Level_Similarity_Perspective_ICCV_2021_paper | Rethinking Self-Supervised Correspondence Learning: A Video Frame-Level Similarity Perspective | [
"Jiarui Xu",
"Xiaolong Wang"
] | https://openaccess.thecvf.com/content/ICCV2021/html/Xu_Rethinking_Self-Supervised_Correspondence_Learning_A_Video_Frame-Level_Similarity_Perspective_ICCV_2021_paper.html | https://openaccess.thecvf.com/content/ICCV2021/papers/Xu_Rethinking_Self-Supervised_Correspondence_Learning_A_Video_Frame-Level_Similarity_Perspective_ICCV_2021_paper.pdf | https://openaccess.thecvf.com/content/ICCV2021/supplemental/Xu_Rethinking_Self-Supervised_Correspondence_ICCV_2021_supplemental.pdf | 2103.17263 | cvf | @InProceedings{Xu_2021_ICCV,
author = {Xu, Jiarui and Wang, Xiaolong},
title = {Rethinking Self-Supervised Correspondence Learning: A Video Frame-Level Similarity Perspective},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
month = {October},
... | Learning a good representation for space-time correspondence is the key for various computer vision tasks, including tracking object bounding boxes and performing video object pixel segmentation. To learn generalizable representation for correspondence in large-scale, a variety of self-supervised pretext tasks are prop... |
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