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Li_Event_Stream_Super-Resolution_via_Spatiotemporal_Constraint_Learning_ICCV_2021_paper | Event Stream Super-Resolution via Spatiotemporal Constraint Learning | [
"Siqi Li",
"Yutong Feng",
"Yipeng Li",
"Yu Jiang",
"Changqing Zou",
"Yue Gao"
] | https://openaccess.thecvf.com/content/ICCV2021/html/Li_Event_Stream_Super-Resolution_via_Spatiotemporal_Constraint_Learning_ICCV_2021_paper.html | https://openaccess.thecvf.com/content/ICCV2021/papers/Li_Event_Stream_Super-Resolution_via_Spatiotemporal_Constraint_Learning_ICCV_2021_paper.pdf | https://openaccess.thecvf.com/content/ICCV2021/supplemental/Li_Event_Stream_Super-Resolution_ICCV_2021_supplemental.zip | null | null | @InProceedings{Li_2021_ICCV,
author = {Li, Siqi and Feng, Yutong and Li, Yipeng and Jiang, Yu and Zou, Changqing and Gao, Yue},
title = {Event Stream Super-Resolution via Spatiotemporal Constraint Learning},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},... | Event cameras are bio-inspired sensors that respond to brightness changes asynchronously and output in the form of event streams instead of frame-based images. They own outstanding advantages compared with traditional cameras: higher temporal resolution, higher dynamic range, and lower power consumption. However, the s... |
Huang_PrimitiveNet_Primitive_Instance_Segmentation_With_Local_Primitive_Embedding_Under_Adversarial_ICCV_2021_paper | PrimitiveNet: Primitive Instance Segmentation With Local Primitive Embedding Under Adversarial Metric | [
"Jingwei Huang",
"Yanfeng Zhang",
"Mingwei Sun"
] | https://openaccess.thecvf.com/content/ICCV2021/html/Huang_PrimitiveNet_Primitive_Instance_Segmentation_With_Local_Primitive_Embedding_Under_Adversarial_ICCV_2021_paper.html | https://openaccess.thecvf.com/content/ICCV2021/papers/Huang_PrimitiveNet_Primitive_Instance_Segmentation_With_Local_Primitive_Embedding_Under_Adversarial_ICCV_2021_paper.pdf | https://openaccess.thecvf.com/content/ICCV2021/supplemental/Huang_PrimitiveNet_Primitive_Instance_ICCV_2021_supplemental.pdf | null | null | @InProceedings{Huang_2021_ICCV,
author = {Huang, Jingwei and Zhang, Yanfeng and Sun, Mingwei},
title = {PrimitiveNet: Primitive Instance Segmentation With Local Primitive Embedding Under Adversarial Metric},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},... | We present PrimitiveNet, a novel approach for high-resolution primitive instance segmentation from point clouds on a large scale. Our key idea is to transform the global segmentation problem into easier local tasks. We train a high-resolution primitive embedding network to predict explicit geometry features and implici... |
Liu_FuseFormer_Fusing_Fine-Grained_Information_in_Transformers_for_Video_Inpainting_ICCV_2021_paper | FuseFormer: Fusing Fine-Grained Information in Transformers for Video Inpainting | [
"Rui Liu",
"Hanming Deng",
"Yangyi Huang",
"Xiaoyu Shi",
"Lewei Lu",
"Wenxiu Sun",
"Xiaogang Wang",
"Jifeng Dai",
"Hongsheng Li"
] | https://openaccess.thecvf.com/content/ICCV2021/html/Liu_FuseFormer_Fusing_Fine-Grained_Information_in_Transformers_for_Video_Inpainting_ICCV_2021_paper.html | https://openaccess.thecvf.com/content/ICCV2021/papers/Liu_FuseFormer_Fusing_Fine-Grained_Information_in_Transformers_for_Video_Inpainting_ICCV_2021_paper.pdf | null | 2109.02974 | cvf | @InProceedings{Liu_2021_ICCV,
author = {Liu, Rui and Deng, Hanming and Huang, Yangyi and Shi, Xiaoyu and Lu, Lewei and Sun, Wenxiu and Wang, Xiaogang and Dai, Jifeng and Li, Hongsheng},
title = {FuseFormer: Fusing Fine-Grained Information in Transformers for Video Inpainting},
booktitle = {Proceeding... | Transformer, as a strong and flexible architecture for modelling long-range relations, has been widely explored in vision tasks. However, when used in video inpainting that requires fine-grained representation, existed method still suffers from yielding blurry edges in detail due to the hard patch splitting. Here we ai... |
Zang_FASA_Feature_Augmentation_and_Sampling_Adaptation_for_Long-Tailed_Instance_Segmentation_ICCV_2021_paper | FASA: Feature Augmentation and Sampling Adaptation for Long-Tailed Instance Segmentation | [
"Yuhang Zang",
"Chen Huang",
"Chen Change Loy"
] | https://openaccess.thecvf.com/content/ICCV2021/html/Zang_FASA_Feature_Augmentation_and_Sampling_Adaptation_for_Long-Tailed_Instance_Segmentation_ICCV_2021_paper.html | https://openaccess.thecvf.com/content/ICCV2021/papers/Zang_FASA_Feature_Augmentation_and_Sampling_Adaptation_for_Long-Tailed_Instance_Segmentation_ICCV_2021_paper.pdf | https://openaccess.thecvf.com/content/ICCV2021/supplemental/Zang_FASA_Feature_Augmentation_ICCV_2021_supplemental.pdf | 2102.12867 | cvf | @InProceedings{Zang_2021_ICCV,
author = {Zang, Yuhang and Huang, Chen and Loy, Chen Change},
title = {FASA: Feature Augmentation and Sampling Adaptation for Long-Tailed Instance Segmentation},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
month ... | Recent methods for long-tailed instance segmentation still struggle on rare object classes with few training data. We propose a simple yet effective method, Feature Augmentation and Sampling Adaptation (FASA), that addresses the data scarcity issue by augmenting the feature space especially for rare classes. Both the F... |
Klopp_Online-Trained_Upsampler_for_Deep_Low_Complexity_Video_Compression_ICCV_2021_paper | Online-Trained Upsampler for Deep Low Complexity Video Compression | [
"Jan P. Klopp",
"Keng-Chi Liu",
"Shao-Yi Chien",
"Liang-Gee Chen"
] | https://openaccess.thecvf.com/content/ICCV2021/html/Klopp_Online-Trained_Upsampler_for_Deep_Low_Complexity_Video_Compression_ICCV_2021_paper.html | https://openaccess.thecvf.com/content/ICCV2021/papers/Klopp_Online-Trained_Upsampler_for_Deep_Low_Complexity_Video_Compression_ICCV_2021_paper.pdf | https://openaccess.thecvf.com/content/ICCV2021/supplemental/Klopp_Online-Trained_Upsampler_for_ICCV_2021_supplemental.pdf | null | null | @InProceedings{Klopp_2021_ICCV,
author = {Klopp, Jan P. and Liu, Keng-Chi and Chien, Shao-Yi and Chen, Liang-Gee},
title = {Online-Trained Upsampler for Deep Low Complexity Video Compression},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
month ... | Deep learning for image and video compression has demonstrated promising results both as a standalone technology and a hybrid combination with existing codecs. However, these systems still come with high computational costs. Deep learning models are typically applied directly in pixel space, making them expensive when ... |
Xiao_DTMNet_A_Discrete_Tchebichef_Moments-Based_Deep_Neural_Network_for_Multi-Focus_ICCV_2021_paper | DTMNet: A Discrete Tchebichef Moments-Based Deep Neural Network for Multi-Focus Image Fusion | [
"Bin Xiao",
"Haifeng Wu",
"Xiuli Bi"
] | https://openaccess.thecvf.com/content/ICCV2021/html/Xiao_DTMNet_A_Discrete_Tchebichef_Moments-Based_Deep_Neural_Network_for_Multi-Focus_ICCV_2021_paper.html | https://openaccess.thecvf.com/content/ICCV2021/papers/Xiao_DTMNet_A_Discrete_Tchebichef_Moments-Based_Deep_Neural_Network_for_Multi-Focus_ICCV_2021_paper.pdf | null | null | null | @InProceedings{Xiao_2021_ICCV,
author = {Xiao, Bin and Wu, Haifeng and Bi, Xiuli},
title = {DTMNet: A Discrete Tchebichef Moments-Based Deep Neural Network for Multi-Focus Image Fusion},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
month = {Oct... | Compared with traditional methods, the deep learning-based multi-focus image fusion methods can effectively improve the performance of image fusion tasks. However, the existing deep learning-based methods encounter a common issue of a large number of parameters, which leads to the deep learning models with high time co... |
Wang_Interactive_Prototype_Learning_for_Egocentric_Action_Recognition_ICCV_2021_paper | Interactive Prototype Learning for Egocentric Action Recognition | [
"Xiaohan Wang",
"Linchao Zhu",
"Heng Wang",
"Yi Yang"
] | https://openaccess.thecvf.com/content/ICCV2021/html/Wang_Interactive_Prototype_Learning_for_Egocentric_Action_Recognition_ICCV_2021_paper.html | https://openaccess.thecvf.com/content/ICCV2021/papers/Wang_Interactive_Prototype_Learning_for_Egocentric_Action_Recognition_ICCV_2021_paper.pdf | null | null | null | @InProceedings{Wang_2021_ICCV,
author = {Wang, Xiaohan and Zhu, Linchao and Wang, Heng and Yang, Yi},
title = {Interactive Prototype Learning for Egocentric Action Recognition},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
month = {October},
... | Egocentric video recognition is a challenging task that requires to identify both the actor's motion and the active object that the actor interacts with. Recognizing the active object is particularly hard due to the cluttered background with distracting objects, the frequent field of view changes, severe occlusion, etc... |
Liu_MBA-VO_Motion_Blur_Aware_Visual_Odometry_ICCV_2021_paper | MBA-VO: Motion Blur Aware Visual Odometry | [
"Peidong Liu",
"Xingxing Zuo",
"Viktor Larsson",
"Marc Pollefeys"
] | https://openaccess.thecvf.com/content/ICCV2021/html/Liu_MBA-VO_Motion_Blur_Aware_Visual_Odometry_ICCV_2021_paper.html | https://openaccess.thecvf.com/content/ICCV2021/papers/Liu_MBA-VO_Motion_Blur_Aware_Visual_Odometry_ICCV_2021_paper.pdf | https://openaccess.thecvf.com/content/ICCV2021/supplemental/Liu_MBA-VO_Motion_Blur_ICCV_2021_supplemental.pdf | 2103.13684 | title_snapshot | @InProceedings{Liu_2021_ICCV,
author = {Liu, Peidong and Zuo, Xingxing and Larsson, Viktor and Pollefeys, Marc},
title = {MBA-VO: Motion Blur Aware Visual Odometry},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
month = {October},
year ... | Motion blur is one of the major challenges remaining for visual odometry methods. In low-light conditions where longer exposure times are necessary, motion blur can appear even for relatively slow camera motions. In this paper we present a novel hybrid visual odometry pipeline with direct approach that explicitly model... |
Cha_Co2L_Contrastive_Continual_Learning_ICCV_2021_paper | Co2L: Contrastive Continual Learning | [
"Hyuntak Cha",
"Jaeho Lee",
"Jinwoo Shin"
] | https://openaccess.thecvf.com/content/ICCV2021/html/Cha_Co2L_Contrastive_Continual_Learning_ICCV_2021_paper.html | https://openaccess.thecvf.com/content/ICCV2021/papers/Cha_Co2L_Contrastive_Continual_Learning_ICCV_2021_paper.pdf | https://openaccess.thecvf.com/content/ICCV2021/supplemental/Cha_Co2L_Contrastive_Continual_ICCV_2021_supplemental.zip | null | null | @InProceedings{Cha_2021_ICCV,
author = {Cha, Hyuntak and Lee, Jaeho and Shin, Jinwoo},
title = {Co2L: Contrastive Continual Learning},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
month = {October},
year = {2021},
pages = {9516... | Recent breakthroughs in self-supervised learning show that such algorithms learn visual representations that can be transferred better to unseen tasks than cross-entropy based methods which rely on task-specific supervision. In this paper, we found that the similar holds in the continual learning context: contrastively... |
Chen_STR-GQN_Scene_Representation_and_Rendering_for_Unknown_Cameras_Based_on_ICCV_2021_paper | STR-GQN: Scene Representation and Rendering for Unknown Cameras Based on Spatial Transformation Routing | [
"Wen-Cheng Chen",
"Min-Chun Hu",
"Chu-Song Chen"
] | https://openaccess.thecvf.com/content/ICCV2021/html/Chen_STR-GQN_Scene_Representation_and_Rendering_for_Unknown_Cameras_Based_on_ICCV_2021_paper.html | https://openaccess.thecvf.com/content/ICCV2021/papers/Chen_STR-GQN_Scene_Representation_and_Rendering_for_Unknown_Cameras_Based_on_ICCV_2021_paper.pdf | https://openaccess.thecvf.com/content/ICCV2021/supplemental/Chen_STR-GQN_Scene_Representation_ICCV_2021_supplemental.zip | 2108.03072 | title_snapshot | @InProceedings{Chen_2021_ICCV,
author = {Chen, Wen-Cheng and Hu, Min-Chun and Chen, Chu-Song},
title = {STR-GQN: Scene Representation and Rendering for Unknown Cameras Based on Spatial Transformation Routing},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)... | Geometry-aware modules are widely applied in recent deep learning architectures for scene representation and rendering. However, these modules require intrinsic camera information that might not be obtained accurately. In this paper, we propose a Spatial Transformation Routing (STR) mechanism to model the spatial prope... |
DeVries_Unconstrained_Scene_Generation_With_Locally_Conditioned_Radiance_Fields_ICCV_2021_paper | Unconstrained Scene Generation With Locally Conditioned Radiance Fields | [
"Terrance DeVries",
"Miguel Angel Bautista",
"Nitish Srivastava",
"Graham W. Taylor",
"Joshua M. Susskind"
] | https://openaccess.thecvf.com/content/ICCV2021/html/DeVries_Unconstrained_Scene_Generation_With_Locally_Conditioned_Radiance_Fields_ICCV_2021_paper.html | https://openaccess.thecvf.com/content/ICCV2021/papers/DeVries_Unconstrained_Scene_Generation_With_Locally_Conditioned_Radiance_Fields_ICCV_2021_paper.pdf | https://openaccess.thecvf.com/content/ICCV2021/supplemental/DeVries_Unconstrained_Scene_Generation_ICCV_2021_supplemental.zip | 2104.00670 | cvf | @InProceedings{DeVries_2021_ICCV,
author = {DeVries, Terrance and Bautista, Miguel Angel and Srivastava, Nitish and Taylor, Graham W. and Susskind, Joshua M.},
title = {Unconstrained Scene Generation With Locally Conditioned Radiance Fields},
booktitle = {Proceedings of the IEEE/CVF International Con... | We tackle the challenge of learning a distribution over complex, realistic, indoor scenes. In this paper, we introduce Generative Scene Networks (GSN), which learns to decompose scenes into a collection of many local radiance fields that can be rendered from a free moving camera. Our model can be used as a prior to gen... |
Zheng_3D_Human_Pose_Estimation_With_Spatial_and_Temporal_Transformers_ICCV_2021_paper | 3D Human Pose Estimation With Spatial and Temporal Transformers | [
"Ce Zheng",
"Sijie Zhu",
"Matias Mendieta",
"Taojiannan Yang",
"Chen Chen",
"Zhengming Ding"
] | https://openaccess.thecvf.com/content/ICCV2021/html/Zheng_3D_Human_Pose_Estimation_With_Spatial_and_Temporal_Transformers_ICCV_2021_paper.html | https://openaccess.thecvf.com/content/ICCV2021/papers/Zheng_3D_Human_Pose_Estimation_With_Spatial_and_Temporal_Transformers_ICCV_2021_paper.pdf | https://openaccess.thecvf.com/content/ICCV2021/supplemental/Zheng_3D_Human_Pose_ICCV_2021_supplemental.pdf | 2103.10455 | cvf | @InProceedings{Zheng_2021_ICCV,
author = {Zheng, Ce and Zhu, Sijie and Mendieta, Matias and Yang, Taojiannan and Chen, Chen and Ding, Zhengming},
title = {3D Human Pose Estimation With Spatial and Temporal Transformers},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vi... | Transformer architectures have become the model of choice in natural language processing and are now being introduced into computer vision tasks such as image classification, object detection, and semantic segmentation. However, in the field of human pose estimation, convolutional architectures still remain dominant. I... |
Xiong_Self-Supervised_Representation_Learning_From_Flow_Equivariance_ICCV_2021_paper | Self-Supervised Representation Learning From Flow Equivariance | [
"Yuwen Xiong",
"Mengye Ren",
"Wenyuan Zeng",
"Raquel Urtasun"
] | https://openaccess.thecvf.com/content/ICCV2021/html/Xiong_Self-Supervised_Representation_Learning_From_Flow_Equivariance_ICCV_2021_paper.html | https://openaccess.thecvf.com/content/ICCV2021/papers/Xiong_Self-Supervised_Representation_Learning_From_Flow_Equivariance_ICCV_2021_paper.pdf | https://openaccess.thecvf.com/content/ICCV2021/supplemental/Xiong_Self-Supervised_Representation_Learning_ICCV_2021_supplemental.zip | 2101.06553 | cvf | @InProceedings{Xiong_2021_ICCV,
author = {Xiong, Yuwen and Ren, Mengye and Zeng, Wenyuan and Urtasun, Raquel},
title = {Self-Supervised Representation Learning From Flow Equivariance},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
month = {Octob... | Self-supervised representation learning is able to learn semantically meaningful features; however, much of its recent success relies on multiple crops of an image with very few objects. Instead of learning view-invariant representation from simple images, humans learn representations in a complex world with changing s... |
Wang_Continual_Learning_for_Image-Based_Camera_Localization_ICCV_2021_paper | Continual Learning for Image-Based Camera Localization | [
"Shuzhe Wang",
"Zakaria Laskar",
"Iaroslav Melekhov",
"Xiaotian Li",
"Juho Kannala"
] | https://openaccess.thecvf.com/content/ICCV2021/html/Wang_Continual_Learning_for_Image-Based_Camera_Localization_ICCV_2021_paper.html | https://openaccess.thecvf.com/content/ICCV2021/papers/Wang_Continual_Learning_for_Image-Based_Camera_Localization_ICCV_2021_paper.pdf | https://openaccess.thecvf.com/content/ICCV2021/supplemental/Wang_Continual_Learning_for_ICCV_2021_supplemental.pdf | 2108.09112 | cvf | @InProceedings{Wang_2021_ICCV,
author = {Wang, Shuzhe and Laskar, Zakaria and Melekhov, Iaroslav and Li, Xiaotian and Kannala, Juho},
title = {Continual Learning for Image-Based Camera Localization},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
mon... | For several emerging technologies such as augmented reality, autonomous driving and robotics, visual localization is a critical component. Directly regressing camera pose/3D scene coordinates from the input image using deep neural networks has shown great potential. However, such methods assume a stationary data distri... |
Zhou_Visual-Textual_Attentive_Semantic_Consistency_for_Medical_Report_Generation_ICCV_2021_paper | Visual-Textual Attentive Semantic Consistency for Medical Report Generation | [
"Yi Zhou",
"Lei Huang",
"Tao Zhou",
"Huazhu Fu",
"Ling Shao"
] | https://openaccess.thecvf.com/content/ICCV2021/html/Zhou_Visual-Textual_Attentive_Semantic_Consistency_for_Medical_Report_Generation_ICCV_2021_paper.html | https://openaccess.thecvf.com/content/ICCV2021/papers/Zhou_Visual-Textual_Attentive_Semantic_Consistency_for_Medical_Report_Generation_ICCV_2021_paper.pdf | https://openaccess.thecvf.com/content/ICCV2021/supplemental/Zhou_Visual-Textual_Attentive_Semantic_ICCV_2021_supplemental.pdf | null | null | @InProceedings{Zhou_2021_ICCV,
author = {Zhou, Yi and Huang, Lei and Zhou, Tao and Fu, Huazhu and Shao, Ling},
title = {Visual-Textual Attentive Semantic Consistency for Medical Report Generation},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
month... | Diagnosing diseases from medical radiographs and writing reports requires professional knowledge and is time-consuming. To address this, automatic medical report generation approaches have recently gained interest. However, identifying diseases as well as correctly predicting their corresponding sizes, locations and ot... |
Li_Revisiting_Stereo_Depth_Estimation_From_a_Sequence-to-Sequence_Perspective_With_Transformers_ICCV_2021_paper | Revisiting Stereo Depth Estimation From a Sequence-to-Sequence Perspective With Transformers | [
"Zhaoshuo Li",
"Xingtong Liu",
"Nathan Drenkow",
"Andy Ding",
"Francis X. Creighton",
"Russell H. Taylor",
"Mathias Unberath"
] | https://openaccess.thecvf.com/content/ICCV2021/html/Li_Revisiting_Stereo_Depth_Estimation_From_a_Sequence-to-Sequence_Perspective_With_Transformers_ICCV_2021_paper.html | https://openaccess.thecvf.com/content/ICCV2021/papers/Li_Revisiting_Stereo_Depth_Estimation_From_a_Sequence-to-Sequence_Perspective_With_Transformers_ICCV_2021_paper.pdf | https://openaccess.thecvf.com/content/ICCV2021/supplemental/Li_Revisiting_Stereo_Depth_ICCV_2021_supplemental.pdf | 2011.02910 | cvf | @InProceedings{Li_2021_ICCV,
author = {Li, Zhaoshuo and Liu, Xingtong and Drenkow, Nathan and Ding, Andy and Creighton, Francis X. and Taylor, Russell H. and Unberath, Mathias},
title = {Revisiting Stereo Depth Estimation From a Sequence-to-Sequence Perspective With Transformers},
booktitle = {Procee... | Stereo depth estimation relies on optimal correspondence matching between pixels on epipolar lines in the left and right images to infer depth. In this work, we revisit the problem from a sequence-to-sequence correspondence perspective to replace cost volume construction with dense pixel matching using position informa... |
Workman_Augmenting_Depth_Estimation_With_Geospatial_Context_ICCV_2021_paper | Augmenting Depth Estimation With Geospatial Context | [
"Scott Workman",
"Hunter Blanton"
] | https://openaccess.thecvf.com/content/ICCV2021/html/Workman_Augmenting_Depth_Estimation_With_Geospatial_Context_ICCV_2021_paper.html | https://openaccess.thecvf.com/content/ICCV2021/papers/Workman_Augmenting_Depth_Estimation_With_Geospatial_Context_ICCV_2021_paper.pdf | https://openaccess.thecvf.com/content/ICCV2021/supplemental/Workman_Augmenting_Depth_Estimation_ICCV_2021_supplemental.pdf | 2109.09879 | cvf | @InProceedings{Workman_2021_ICCV,
author = {Workman, Scott and Blanton, Hunter},
title = {Augmenting Depth Estimation With Geospatial Context},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
month = {October},
year = {2021},
pages ... | Modern cameras are equipped with a wide array of sensors that enable recording the geospatial context of an image. Taking advantage of this, we explore depth estimation under the assumption that the camera is geocalibrated, a problem we refer to as geo-enabled depth estimation. Our key insight is that if capture locati... |
Lerman_Explaining_Local_Global_and_Higher-Order_Interactions_in_Deep_Learning_ICCV_2021_paper | Explaining Local, Global, and Higher-Order Interactions in Deep Learning | [
"Samuel Lerman",
"Charles Venuto",
"Henry Kautz",
"Chenliang Xu"
] | https://openaccess.thecvf.com/content/ICCV2021/html/Lerman_Explaining_Local_Global_and_Higher-Order_Interactions_in_Deep_Learning_ICCV_2021_paper.html | https://openaccess.thecvf.com/content/ICCV2021/papers/Lerman_Explaining_Local_Global_and_Higher-Order_Interactions_in_Deep_Learning_ICCV_2021_paper.pdf | https://openaccess.thecvf.com/content/ICCV2021/supplemental/Lerman_Explaining_Local_Global_ICCV_2021_supplemental.zip | 2006.08601 | cvf | @InProceedings{Lerman_2021_ICCV,
author = {Lerman, Samuel and Venuto, Charles and Kautz, Henry and Xu, Chenliang},
title = {Explaining Local, Global, and Higher-Order Interactions in Deep Learning},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
mont... | We present a simple yet highly generalizable method for explaining interacting parts within a neural network's reasoning process. First, we design an algorithm based on cross derivatives for computing statistical interaction effects between individual features, which is generalized to both 2-way and higher-order (3-way... |
Hou_Learning_Attribute-Driven_Disentangled_Representations_for_Interactive_Fashion_Retrieval_ICCV_2021_paper | Learning Attribute-Driven Disentangled Representations for Interactive Fashion Retrieval | [
"Yuxin Hou",
"Eleonora Vig",
"Michael Donoser",
"Loris Bazzani"
] | https://openaccess.thecvf.com/content/ICCV2021/html/Hou_Learning_Attribute-Driven_Disentangled_Representations_for_Interactive_Fashion_Retrieval_ICCV_2021_paper.html | https://openaccess.thecvf.com/content/ICCV2021/papers/Hou_Learning_Attribute-Driven_Disentangled_Representations_for_Interactive_Fashion_Retrieval_ICCV_2021_paper.pdf | https://openaccess.thecvf.com/content/ICCV2021/supplemental/Hou_Learning_Attribute-Driven_Disentangled_ICCV_2021_supplemental.pdf | null | null | @InProceedings{Hou_2021_ICCV,
author = {Hou, Yuxin and Vig, Eleonora and Donoser, Michael and Bazzani, Loris},
title = {Learning Attribute-Driven Disentangled Representations for Interactive Fashion Retrieval},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV... | Interactive retrieval for online fashion shopping provides the ability of changing image retrieval results according to the user feedback. One common problem in interactive retrieval is that a specific user interaction (e.g., changing the color of a T-shirt) causes other aspects to change inadvertently (e.g., the resul... |
Yang_SemiHand_Semi-Supervised_Hand_Pose_Estimation_With_Consistency_ICCV_2021_paper | SemiHand: Semi-Supervised Hand Pose Estimation With Consistency | [
"Linlin Yang",
"Shicheng Chen",
"Angela Yao"
] | https://openaccess.thecvf.com/content/ICCV2021/html/Yang_SemiHand_Semi-Supervised_Hand_Pose_Estimation_With_Consistency_ICCV_2021_paper.html | https://openaccess.thecvf.com/content/ICCV2021/papers/Yang_SemiHand_Semi-Supervised_Hand_Pose_Estimation_With_Consistency_ICCV_2021_paper.pdf | https://openaccess.thecvf.com/content/ICCV2021/supplemental/Yang_SemiHand_Semi-Supervised_Hand_ICCV_2021_supplemental.pdf | null | null | @InProceedings{Yang_2021_ICCV,
author = {Yang, Linlin and Chen, Shicheng and Yao, Angela},
title = {SemiHand: Semi-Supervised Hand Pose Estimation With Consistency},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
month = {October},
year ... | We present SemiHand, a semi-supervised framework for 3D hand pose estimation from monocular images. We pre-train the model on labelled synthetic data and fine-tune it on unlabelled real-world data by pseudo-labeling with consistency training. By design, we introduce data augmentation of differing difficulties, consiste... |
Kim_Efficient_Action_Recognition_via_Dynamic_Knowledge_Propagation_ICCV_2021_paper | Efficient Action Recognition via Dynamic Knowledge Propagation | [
"Hanul Kim",
"Mihir Jain",
"Jun-Tae Lee",
"Sungrack Yun",
"Fatih Porikli"
] | https://openaccess.thecvf.com/content/ICCV2021/html/Kim_Efficient_Action_Recognition_via_Dynamic_Knowledge_Propagation_ICCV_2021_paper.html | https://openaccess.thecvf.com/content/ICCV2021/papers/Kim_Efficient_Action_Recognition_via_Dynamic_Knowledge_Propagation_ICCV_2021_paper.pdf | https://openaccess.thecvf.com/content/ICCV2021/supplemental/Kim_Efficient_Action_Recognition_ICCV_2021_supplemental.pdf | null | null | @InProceedings{Kim_2021_ICCV,
author = {Kim, Hanul and Jain, Mihir and Lee, Jun-Tae and Yun, Sungrack and Porikli, Fatih},
title = {Efficient Action Recognition via Dynamic Knowledge Propagation},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
month ... | Efficient action recognition has become crucial to extend the success of action recognition to many real-world applications. Contrary to most existing methods, which mainly focus on selecting salient frames to reduce the computation cost, we focus more on making the most of the selected frames. To this end, we employ t... |
Abrahamyan_Bias_Loss_for_Mobile_Neural_Networks_ICCV_2021_paper | Bias Loss for Mobile Neural Networks | [
"Lusine Abrahamyan",
"Valentin Ziatchin",
"Yiming Chen",
"Nikos Deligiannis"
] | https://openaccess.thecvf.com/content/ICCV2021/html/Abrahamyan_Bias_Loss_for_Mobile_Neural_Networks_ICCV_2021_paper.html | https://openaccess.thecvf.com/content/ICCV2021/papers/Abrahamyan_Bias_Loss_for_Mobile_Neural_Networks_ICCV_2021_paper.pdf | https://openaccess.thecvf.com/content/ICCV2021/supplemental/Abrahamyan_Bias_Loss_for_ICCV_2021_supplemental.zip | 2107.11170 | cvf | @InProceedings{Abrahamyan_2021_ICCV,
author = {Abrahamyan, Lusine and Ziatchin, Valentin and Chen, Yiming and Deligiannis, Nikos},
title = {Bias Loss for Mobile Neural Networks},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
month = {October},
... | Compact convolutional neural networks (CNNs) have witnessed exceptional improvements in performance in recent years. However, they still fail to provide the same predictive power as CNNs with a large number of parameters. The diverse and even abundant features captured by the layers is an important characteristic of th... |
Chatterjee_Visual_Scene_Graphs_for_Audio_Source_Separation_ICCV_2021_paper | Visual Scene Graphs for Audio Source Separation | [
"Moitreya Chatterjee",
"Jonathan Le Roux",
"Narendra Ahuja",
"Anoop Cherian"
] | https://openaccess.thecvf.com/content/ICCV2021/html/Chatterjee_Visual_Scene_Graphs_for_Audio_Source_Separation_ICCV_2021_paper.html | https://openaccess.thecvf.com/content/ICCV2021/papers/Chatterjee_Visual_Scene_Graphs_for_Audio_Source_Separation_ICCV_2021_paper.pdf | https://openaccess.thecvf.com/content/ICCV2021/supplemental/Chatterjee_Visual_Scene_Graphs_ICCV_2021_supplemental.pdf | 2109.11955 | cvf | @InProceedings{Chatterjee_2021_ICCV,
author = {Chatterjee, Moitreya and Le Roux, Jonathan and Ahuja, Narendra and Cherian, Anoop},
title = {Visual Scene Graphs for Audio Source Separation},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
month = {... | State-of-the-art approaches for visually-guided audio source separation typically assume sources that have characteristic sounds, such as musical instruments. These approaches often ignore the visual context of these sound sources or avoid modeling object interactions that may be useful to better characterize the sourc... |
Rodriguez_Beyond_Trivial_Counterfactual_Explanations_With_Diverse_Valuable_Explanations_ICCV_2021_paper | Beyond Trivial Counterfactual Explanations With Diverse Valuable Explanations | [
"Pau Rodríguez",
"Massimo Caccia",
"Alexandre Lacoste",
"Lee Zamparo",
"Issam Laradji",
"Laurent Charlin",
"David Vazquez"
] | https://openaccess.thecvf.com/content/ICCV2021/html/Rodriguez_Beyond_Trivial_Counterfactual_Explanations_With_Diverse_Valuable_Explanations_ICCV_2021_paper.html | https://openaccess.thecvf.com/content/ICCV2021/papers/Rodriguez_Beyond_Trivial_Counterfactual_Explanations_With_Diverse_Valuable_Explanations_ICCV_2021_paper.pdf | https://openaccess.thecvf.com/content/ICCV2021/supplemental/Rodriguez_Beyond_Trivial_Counterfactual_ICCV_2021_supplemental.pdf | 2103.10226 | title_snapshot | @InProceedings{Rodriguez_2021_ICCV,
author = {Rodr{\'\i}guez, Pau and Caccia, Massimo and Lacoste, Alexandre and Zamparo, Lee and Laradji, Issam and Charlin, Laurent and Vazquez, David},
title = {Beyond Trivial Counterfactual Explanations With Diverse Valuable Explanations},
booktitle = {Proceedings ... | Explainability for machine learning models has gained considerable attention within the research community given the importance of deploying more reliable machine-learning systems. In computer vision applications, generative counterfactual methods indicate how to perturb a model's input to change its prediction, provid... |
Liu_Homogeneous_Architecture_Augmentation_for_Neural_Predictor_ICCV_2021_paper | Homogeneous Architecture Augmentation for Neural Predictor | [
"Yuqiao Liu",
"Yehui Tang",
"Yanan Sun"
] | https://openaccess.thecvf.com/content/ICCV2021/html/Liu_Homogeneous_Architecture_Augmentation_for_Neural_Predictor_ICCV_2021_paper.html | https://openaccess.thecvf.com/content/ICCV2021/papers/Liu_Homogeneous_Architecture_Augmentation_for_Neural_Predictor_ICCV_2021_paper.pdf | null | 2107.13153 | cvf | @InProceedings{Liu_2021_ICCV,
author = {Liu, Yuqiao and Tang, Yehui and Sun, Yanan},
title = {Homogeneous Architecture Augmentation for Neural Predictor},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
month = {October},
year = {2021},
... | Neural Architecture Search (NAS) can automatically design well-performed architectures of Deep Neural Networks (DNNs) for the tasks at hand. However, one bottleneck of NAS is the prohibitively computational cost largely due to the expensive performance evaluation. The neural predictors can directly estimate the perform... |
Xu_Co-Scale_Conv-Attentional_Image_Transformers_ICCV_2021_paper | Co-Scale Conv-Attentional Image Transformers | [
"Weijian Xu",
"Yifan Xu",
"Tyler Chang",
"Zhuowen Tu"
] | https://openaccess.thecvf.com/content/ICCV2021/html/Xu_Co-Scale_Conv-Attentional_Image_Transformers_ICCV_2021_paper.html | https://openaccess.thecvf.com/content/ICCV2021/papers/Xu_Co-Scale_Conv-Attentional_Image_Transformers_ICCV_2021_paper.pdf | null | 2104.06399 | cvf | @InProceedings{Xu_2021_ICCV,
author = {Xu, Weijian and Xu, Yifan and Chang, Tyler and Tu, Zhuowen},
title = {Co-Scale Conv-Attentional Image Transformers},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
month = {October},
year = {2021},
... | In this paper, we present Co-scale conv-attentional image Transformers (CoaT), a Transformer-based image classifier equipped with co-scale and conv-attentional mechanisms. First, the co-scale mechanism maintains the integrity of Transformers' encoder branches at individual scales, while allowing representations learned... |
Vasconcelos_Impact_of_Aliasing_on_Generalization_in_Deep_Convolutional_Networks_ICCV_2021_paper | Impact of Aliasing on Generalization in Deep Convolutional Networks | [
"Cristina Vasconcelos",
"Hugo Larochelle",
"Vincent Dumoulin",
"Rob Romijnders",
"Nicolas Le Roux",
"Ross Goroshin"
] | https://openaccess.thecvf.com/content/ICCV2021/html/Vasconcelos_Impact_of_Aliasing_on_Generalization_in_Deep_Convolutional_Networks_ICCV_2021_paper.html | https://openaccess.thecvf.com/content/ICCV2021/papers/Vasconcelos_Impact_of_Aliasing_on_Generalization_in_Deep_Convolutional_Networks_ICCV_2021_paper.pdf | https://openaccess.thecvf.com/content/ICCV2021/supplemental/Vasconcelos_Impact_of_Aliasing_ICCV_2021_supplemental.pdf | 2108.03489 | cvf | @InProceedings{Vasconcelos_2021_ICCV,
author = {Vasconcelos, Cristina and Larochelle, Hugo and Dumoulin, Vincent and Romijnders, Rob and Le Roux, Nicolas and Goroshin, Ross},
title = {Impact of Aliasing on Generalization in Deep Convolutional Networks},
booktitle = {Proceedings of the IEEE/CVF Intern... | We investigate the impact of aliasing on generalization in Deep Convolutional Networks and show that data augmentation schemes alone are unable to prevent it due to structural limitations in widely used architectures. Drawing insights from frequency analysis theory, we take a closer look at Resnet and EfficientNet arch... |
Kocabas_PARE_Part_Attention_Regressor_for_3D_Human_Body_Estimation_ICCV_2021_paper | PARE: Part Attention Regressor for 3D Human Body Estimation | [
"Muhammed Kocabas",
"Chun-Hao P. Huang",
"Otmar Hilliges",
"Michael J. Black"
] | https://openaccess.thecvf.com/content/ICCV2021/html/Kocabas_PARE_Part_Attention_Regressor_for_3D_Human_Body_Estimation_ICCV_2021_paper.html | https://openaccess.thecvf.com/content/ICCV2021/papers/Kocabas_PARE_Part_Attention_Regressor_for_3D_Human_Body_Estimation_ICCV_2021_paper.pdf | https://openaccess.thecvf.com/content/ICCV2021/supplemental/Kocabas_PARE_Part_Attention_ICCV_2021_supplemental.pdf | 2104.08527 | cvf | @InProceedings{Kocabas_2021_ICCV,
author = {Kocabas, Muhammed and Huang, Chun-Hao P. and Hilliges, Otmar and Black, Michael J.},
title = {PARE: Part Attention Regressor for 3D Human Body Estimation},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
mon... | Despite significant progress, we show that state of the art 3D human pose and shape estimation methods remain sensitive to partial occlusion and can produce dramatically wrong predictions although much of the body is observable. To address this, we introduce a soft attention mechanism, called the Part Attention REgress... |
Iwase_RePOSE_Fast_6D_Object_Pose_Refinement_via_Deep_Texture_Rendering_ICCV_2021_paper | RePOSE: Fast 6D Object Pose Refinement via Deep Texture Rendering | [
"Shun Iwase",
"Xingyu Liu",
"Rawal Khirodkar",
"Rio Yokota",
"Kris M. Kitani"
] | https://openaccess.thecvf.com/content/ICCV2021/html/Iwase_RePOSE_Fast_6D_Object_Pose_Refinement_via_Deep_Texture_Rendering_ICCV_2021_paper.html | https://openaccess.thecvf.com/content/ICCV2021/papers/Iwase_RePOSE_Fast_6D_Object_Pose_Refinement_via_Deep_Texture_Rendering_ICCV_2021_paper.pdf | https://openaccess.thecvf.com/content/ICCV2021/supplemental/Iwase_RePOSE_Fast_6D_ICCV_2021_supplemental.pdf | 2104.00633 | cvf | @InProceedings{Iwase_2021_ICCV,
author = {Iwase, Shun and Liu, Xingyu and Khirodkar, Rawal and Yokota, Rio and Kitani, Kris M.},
title = {RePOSE: Fast 6D Object Pose Refinement via Deep Texture Rendering},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
... | We present RePOSE, a fast iterative refinement method for 6D object pose estimation. Prior methods perform refinement by feeding zoomed-in input and rendered RGB images into a CNN and directly regressing an update of a refined pose. Their runtime is slow due to the computational cost of CNN, which is especially promine... |
Kopuklu_How_To_Design_a_Three-Stage_Architecture_for_Audio-Visual_Active_Speaker_ICCV_2021_paper | How To Design a Three-Stage Architecture for Audio-Visual Active Speaker Detection in the Wild | [
"Okan Köpüklü",
"Maja Taseska",
"Gerhard Rigoll"
] | https://openaccess.thecvf.com/content/ICCV2021/html/Kopuklu_How_To_Design_a_Three-Stage_Architecture_for_Audio-Visual_Active_Speaker_ICCV_2021_paper.html | https://openaccess.thecvf.com/content/ICCV2021/papers/Kopuklu_How_To_Design_a_Three-Stage_Architecture_for_Audio-Visual_Active_Speaker_ICCV_2021_paper.pdf | null | 2106.03932 | title_snapshot | @InProceedings{Kopuklu_2021_ICCV,
author = {K\"op\"ukl\"u, Okan and Taseska, Maja and Rigoll, Gerhard},
title = {How To Design a Three-Stage Architecture for Audio-Visual Active Speaker Detection in the Wild},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)... | Successful active speaker detection requires a three-stage pipeline: (i) audio-visual encoding for all speakers in the clip, (ii) inter-speaker relation modeling between a reference speaker and the background speakers within each frame, and (iii) temporal modeling for the reference speaker. Each stage of this pipeline ... |
Kobs_Do_Different_Deep_Metric_Learning_Losses_Lead_to_Similar_Learned_ICCV_2021_paper | Do Different Deep Metric Learning Losses Lead to Similar Learned Features? | [
"Konstantin Kobs",
"Michael Steininger",
"Andrzej Dulny",
"Andreas Hotho"
] | https://openaccess.thecvf.com/content/ICCV2021/html/Kobs_Do_Different_Deep_Metric_Learning_Losses_Lead_to_Similar_Learned_ICCV_2021_paper.html | https://openaccess.thecvf.com/content/ICCV2021/papers/Kobs_Do_Different_Deep_Metric_Learning_Losses_Lead_to_Similar_Learned_ICCV_2021_paper.pdf | https://openaccess.thecvf.com/content/ICCV2021/supplemental/Kobs_Do_Different_Deep_ICCV_2021_supplemental.pdf | 2205.02698 | title_snapshot | @InProceedings{Kobs_2021_ICCV,
author = {Kobs, Konstantin and Steininger, Michael and Dulny, Andrzej and Hotho, Andreas},
title = {Do Different Deep Metric Learning Losses Lead to Similar Learned Features?},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},... | Recent studies have shown that many deep metric learning loss functions perform very similarly under the same experimental conditions. One potential reason for this unexpected result is that all losses let the network focus on similar image regions or properties. In this paper, we investigate this by conducting a two-s... |
Yang_Just_Ask_Learning_To_Answer_Questions_From_Millions_of_Narrated_ICCV_2021_paper | Just Ask: Learning To Answer Questions From Millions of Narrated Videos | [
"Antoine Yang",
"Antoine Miech",
"Josef Sivic",
"Ivan Laptev",
"Cordelia Schmid"
] | https://openaccess.thecvf.com/content/ICCV2021/html/Yang_Just_Ask_Learning_To_Answer_Questions_From_Millions_of_Narrated_ICCV_2021_paper.html | https://openaccess.thecvf.com/content/ICCV2021/papers/Yang_Just_Ask_Learning_To_Answer_Questions_From_Millions_of_Narrated_ICCV_2021_paper.pdf | https://openaccess.thecvf.com/content/ICCV2021/supplemental/Yang_Just_Ask_Learning_ICCV_2021_supplemental.pdf | 2012.00451 | cvf | @InProceedings{Yang_2021_ICCV,
author = {Yang, Antoine and Miech, Antoine and Sivic, Josef and Laptev, Ivan and Schmid, Cordelia},
title = {Just Ask: Learning To Answer Questions From Millions of Narrated Videos},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (I... | Recent methods for visual question answering rely on large-scale annotated datasets. Manual annotation of questions and answers for videos, however, is tedious, expensive and prevents scalability. In this work, we propose to avoid manual annotation and generate a large-scale training dataset for video question answerin... |
Yang_Towards_Face_Encryption_by_Generating_Adversarial_Identity_Masks_ICCV_2021_paper | Towards Face Encryption by Generating Adversarial Identity Masks | [
"Xiao Yang",
"Yinpeng Dong",
"Tianyu Pang",
"Hang Su",
"Jun Zhu",
"Yuefeng Chen",
"Hui Xue"
] | https://openaccess.thecvf.com/content/ICCV2021/html/Yang_Towards_Face_Encryption_by_Generating_Adversarial_Identity_Masks_ICCV_2021_paper.html | https://openaccess.thecvf.com/content/ICCV2021/papers/Yang_Towards_Face_Encryption_by_Generating_Adversarial_Identity_Masks_ICCV_2021_paper.pdf | https://openaccess.thecvf.com/content/ICCV2021/supplemental/Yang_Towards_Face_Encryption_ICCV_2021_supplemental.pdf | 2003.06814 | cvf | @InProceedings{Yang_2021_ICCV,
author = {Yang, Xiao and Dong, Yinpeng and Pang, Tianyu and Su, Hang and Zhu, Jun and Chen, Yuefeng and Xue, Hui},
title = {Towards Face Encryption by Generating Adversarial Identity Masks},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer V... | As billions of personal data being shared through social media and network, the data privacy and security have drawn an increasing attention. Several attempts have been made to alleviate the leakage of identity information from face photos, with the aid of, e.g., image obfuscation techniques. However, most of the prese... |
Hu_UniT_Multimodal_Multitask_Learning_With_a_Unified_Transformer_ICCV_2021_paper | UniT: Multimodal Multitask Learning With a Unified Transformer | [
"Ronghang Hu",
"Amanpreet Singh"
] | https://openaccess.thecvf.com/content/ICCV2021/html/Hu_UniT_Multimodal_Multitask_Learning_With_a_Unified_Transformer_ICCV_2021_paper.html | https://openaccess.thecvf.com/content/ICCV2021/papers/Hu_UniT_Multimodal_Multitask_Learning_With_a_Unified_Transformer_ICCV_2021_paper.pdf | https://openaccess.thecvf.com/content/ICCV2021/supplemental/Hu_UniT_Multimodal_Multitask_ICCV_2021_supplemental.pdf | 2102.10772 | cvf | @InProceedings{Hu_2021_ICCV,
author = {Hu, Ronghang and Singh, Amanpreet},
title = {UniT: Multimodal Multitask Learning With a Unified Transformer},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
month = {October},
year = {2021},
pag... | We propose UniT, a Unified Transformer model to simultaneously learn the most prominent tasks across different domains, ranging from object detection to natural language understanding and multimodal reasoning. Based on the transformer encoder-decoder architecture, our UniT model encodes each input modality with an enco... |
Ren_CSG-Stump_A_Learning_Friendly_CSG-Like_Representation_for_Interpretable_Shape_Parsing_ICCV_2021_paper | CSG-Stump: A Learning Friendly CSG-Like Representation for Interpretable Shape Parsing | [
"Daxuan Ren",
"Jianmin Zheng",
"Jianfei Cai",
"Jiatong Li",
"Haiyong Jiang",
"Zhongang Cai",
"Junzhe Zhang",
"Liang Pan",
"Mingyuan Zhang",
"Haiyu Zhao",
"Shuai Yi"
] | https://openaccess.thecvf.com/content/ICCV2021/html/Ren_CSG-Stump_A_Learning_Friendly_CSG-Like_Representation_for_Interpretable_Shape_Parsing_ICCV_2021_paper.html | https://openaccess.thecvf.com/content/ICCV2021/papers/Ren_CSG-Stump_A_Learning_Friendly_CSG-Like_Representation_for_Interpretable_Shape_Parsing_ICCV_2021_paper.pdf | https://openaccess.thecvf.com/content/ICCV2021/supplemental/Ren_CSG-Stump_A_Learning_ICCV_2021_supplemental.pdf | 2108.11305 | title_snapshot | @InProceedings{Ren_2021_ICCV,
author = {Ren, Daxuan and Zheng, Jianmin and Cai, Jianfei and Li, Jiatong and Jiang, Haiyong and Cai, Zhongang and Zhang, Junzhe and Pan, Liang and Zhang, Mingyuan and Zhao, Haiyu and Yi, Shuai},
title = {CSG-Stump: A Learning Friendly CSG-Like Representation for Interpretab... | Generating an interpretable and compact representation of 3D shapes from point clouds is an important and challenging problem. This paper presents CSG-Stump Net, an unsupervised end-to-end network for learning shapes from point clouds and discovering the underlying constituent modeling primitives and operations as well... |
Fang_Compressing_Visual-Linguistic_Model_via_Knowledge_Distillation_ICCV_2021_paper | Compressing Visual-Linguistic Model via Knowledge Distillation | [
"Zhiyuan Fang",
"Jianfeng Wang",
"Xiaowei Hu",
"Lijuan Wang",
"Yezhou Yang",
"Zicheng Liu"
] | https://openaccess.thecvf.com/content/ICCV2021/html/Fang_Compressing_Visual-Linguistic_Model_via_Knowledge_Distillation_ICCV_2021_paper.html | https://openaccess.thecvf.com/content/ICCV2021/papers/Fang_Compressing_Visual-Linguistic_Model_via_Knowledge_Distillation_ICCV_2021_paper.pdf | null | 2104.02096 | cvf | @InProceedings{Fang_2021_ICCV,
author = {Fang, Zhiyuan and Wang, Jianfeng and Hu, Xiaowei and Wang, Lijuan and Yang, Yezhou and Liu, Zicheng},
title = {Compressing Visual-Linguistic Model via Knowledge Distillation},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision... | Despite exciting progress in pre-training for visual-linguistic (VL) representations, very few aspire to a small VL model. In this paper, we study knowledge distillation(KD) to effectively compress a transformer-based large VL model into a small VL model. The major challenge arises from the inconsistent regional visual... |
Ji_Full-Duplex_Strategy_for_Video_Object_Segmentation_ICCV_2021_paper | Full-Duplex Strategy for Video Object Segmentation | [
"Ge-Peng Ji",
"Keren Fu",
"Zhe Wu",
"Deng-Ping Fan",
"Jianbing Shen",
"Ling Shao"
] | https://openaccess.thecvf.com/content/ICCV2021/html/Ji_Full-Duplex_Strategy_for_Video_Object_Segmentation_ICCV_2021_paper.html | https://openaccess.thecvf.com/content/ICCV2021/papers/Ji_Full-Duplex_Strategy_for_Video_Object_Segmentation_ICCV_2021_paper.pdf | null | 2108.03151 | cvf | @InProceedings{Ji_2021_ICCV,
author = {Ji, Ge-Peng and Fu, Keren and Wu, Zhe and Fan, Deng-Ping and Shen, Jianbing and Shao, Ling},
title = {Full-Duplex Strategy for Video Object Segmentation},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
month ... | Appearance and motion are two important sources of information in video object segmentation (VOS). Previous methods mainly focus on using simplex solutions, lowering the upper bound of feature collaboration among and across these two cues. In this paper, we study a novel framework, termed the FSNet (Full-duplex Strateg... |
Assran_Semi-Supervised_Learning_of_Visual_Features_by_Non-Parametrically_Predicting_View_Assignments_ICCV_2021_paper | Semi-Supervised Learning of Visual Features by Non-Parametrically Predicting View Assignments With Support Samples | [
"Mahmoud Assran",
"Mathilde Caron",
"Ishan Misra",
"Piotr Bojanowski",
"Armand Joulin",
"Nicolas Ballas",
"Michael Rabbat"
] | https://openaccess.thecvf.com/content/ICCV2021/html/Assran_Semi-Supervised_Learning_of_Visual_Features_by_Non-Parametrically_Predicting_View_Assignments_ICCV_2021_paper.html | https://openaccess.thecvf.com/content/ICCV2021/papers/Assran_Semi-Supervised_Learning_of_Visual_Features_by_Non-Parametrically_Predicting_View_Assignments_ICCV_2021_paper.pdf | https://openaccess.thecvf.com/content/ICCV2021/supplemental/Assran_Semi-Supervised_Learning_of_ICCV_2021_supplemental.pdf | 2104.13963 | cvf | @InProceedings{Assran_2021_ICCV,
author = {Assran, Mahmoud and Caron, Mathilde and Misra, Ishan and Bojanowski, Piotr and Joulin, Armand and Ballas, Nicolas and Rabbat, Michael},
title = {Semi-Supervised Learning of Visual Features by Non-Parametrically Predicting View Assignments With Support Samples},
... | This paper proposes a novel method of learning by predicting view assignments with support samples (PAWS). The method trains a model to minimize a consistency loss, which ensures that different views of the same unlabeled instance are assigned similar pseudo-labels. The pseudo-labels are generated non-parametrically, b... |
Li_Unsupervised_Non-Rigid_Image_Distortion_Removal_via_Grid_Deformation_ICCV_2021_paper | Unsupervised Non-Rigid Image Distortion Removal via Grid Deformation | [
"Nianyi Li",
"Simron Thapa",
"Cameron Whyte",
"Albert W. Reed",
"Suren Jayasuriya",
"Jinwei Ye"
] | https://openaccess.thecvf.com/content/ICCV2021/html/Li_Unsupervised_Non-Rigid_Image_Distortion_Removal_via_Grid_Deformation_ICCV_2021_paper.html | https://openaccess.thecvf.com/content/ICCV2021/papers/Li_Unsupervised_Non-Rigid_Image_Distortion_Removal_via_Grid_Deformation_ICCV_2021_paper.pdf | https://openaccess.thecvf.com/content/ICCV2021/supplemental/Li_Unsupervised_Non-Rigid_Image_ICCV_2021_supplemental.zip | null | null | @InProceedings{Li_2021_ICCV,
author = {Li, Nianyi and Thapa, Simron and Whyte, Cameron and Reed, Albert W. and Jayasuriya, Suren and Ye, Jinwei},
title = {Unsupervised Non-Rigid Image Distortion Removal via Grid Deformation},
booktitle = {Proceedings of the IEEE/CVF International Conference on Comput... | Many computer vision problems face difficulties when imaging through turbulent refractive media (e.g., air and water) due to the refraction and scattering of light. These effects cause geometric distortion that requires either handcrafted physical priors or supervised learning methods to remove. In this paper, we prese... |
Voskou_Stochastic_Transformer_Networks_With_Linear_Competing_Units_Application_To_End-to-End_ICCV_2021_paper | Stochastic Transformer Networks With Linear Competing Units: Application To End-to-End SL Translation | [
"Andreas Voskou",
"Konstantinos P. Panousis",
"Dimitrios Kosmopoulos",
"Dimitris N. Metaxas",
"Sotirios Chatzis"
] | https://openaccess.thecvf.com/content/ICCV2021/html/Voskou_Stochastic_Transformer_Networks_With_Linear_Competing_Units_Application_To_End-to-End_ICCV_2021_paper.html | https://openaccess.thecvf.com/content/ICCV2021/papers/Voskou_Stochastic_Transformer_Networks_With_Linear_Competing_Units_Application_To_End-to-End_ICCV_2021_paper.pdf | https://openaccess.thecvf.com/content/ICCV2021/supplemental/Voskou_Stochastic_Transformer_Networks_ICCV_2021_supplemental.pdf | 2109.13318 | cvf | @InProceedings{Voskou_2021_ICCV,
author = {Voskou, Andreas and Panousis, Konstantinos P. and Kosmopoulos, Dimitrios and Metaxas, Dimitris N. and Chatzis, Sotirios},
title = {Stochastic Transformer Networks With Linear Competing Units: Application To End-to-End SL Translation},
booktitle = {Proceeding... | Automating sign language translation (SLT) is a challenging real-world application. Despite its societal importance, though, research progress in the field remains rather poor. Crucially, existing methods that yield viable performance necessitate the availability of laborious to obtain gloss sequence groundtruth. In th... |
Verelst_BlockCopy_High-Resolution_Video_Processing_With_Block-Sparse_Feature_Propagation_and_Online_ICCV_2021_paper | BlockCopy: High-Resolution Video Processing With Block-Sparse Feature Propagation and Online Policies | [
"Thomas Verelst",
"Tinne Tuytelaars"
] | https://openaccess.thecvf.com/content/ICCV2021/html/Verelst_BlockCopy_High-Resolution_Video_Processing_With_Block-Sparse_Feature_Propagation_and_Online_ICCV_2021_paper.html | https://openaccess.thecvf.com/content/ICCV2021/papers/Verelst_BlockCopy_High-Resolution_Video_Processing_With_Block-Sparse_Feature_Propagation_and_Online_ICCV_2021_paper.pdf | https://openaccess.thecvf.com/content/ICCV2021/supplemental/Verelst_BlockCopy_High-Resolution_Video_ICCV_2021_supplemental.zip | 2108.09376 | cvf | @InProceedings{Verelst_2021_ICCV,
author = {Verelst, Thomas and Tuytelaars, Tinne},
title = {BlockCopy: High-Resolution Video Processing With Block-Sparse Feature Propagation and Online Policies},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
month ... | In this paper we propose BlockCopy, a scheme that accelerates pretrained frame-based CNNs to process video more efficiently, compared to standard frame-by-frame processing. To this end, a lightweight policy network determines important regions in an image, and operations are applied on selected regions only, using cust... |
Changpinyo_Telling_the_What_While_Pointing_to_the_Where_Multimodal_Queries_ICCV_2021_paper | Telling the What While Pointing to the Where: Multimodal Queries for Image Retrieval | [
"Soravit Changpinyo",
"Jordi Pont-Tuset",
"Vittorio Ferrari",
"Radu Soricut"
] | https://openaccess.thecvf.com/content/ICCV2021/html/Changpinyo_Telling_the_What_While_Pointing_to_the_Where_Multimodal_Queries_ICCV_2021_paper.html | https://openaccess.thecvf.com/content/ICCV2021/papers/Changpinyo_Telling_the_What_While_Pointing_to_the_Where_Multimodal_Queries_ICCV_2021_paper.pdf | https://openaccess.thecvf.com/content/ICCV2021/supplemental/Changpinyo_Telling_the_What_ICCV_2021_supplemental.pdf | 2102.04980 | title_snapshot | @InProceedings{Changpinyo_2021_ICCV,
author = {Changpinyo, Soravit and Pont-Tuset, Jordi and Ferrari, Vittorio and Soricut, Radu},
title = {Telling the What While Pointing to the Where: Multimodal Queries for Image Retrieval},
booktitle = {Proceedings of the IEEE/CVF International Conference on Compu... | Most existing image retrieval systems use text queries as a way for the user to express what they are looking for. However, fine-grained image retrieval often requires the ability to also express where in the image the content they are looking for is. The text modality can only cumbersomely express such localization pr... |
Chen_Unsupervised_Learning_of_Fine_Structure_Generation_for_3D_Point_Clouds_ICCV_2021_paper | Unsupervised Learning of Fine Structure Generation for 3D Point Clouds by 2D Projections Matching | [
"Chao Chen",
"Zhizhong Han",
"Yu-Shen Liu",
"Matthias Zwicker"
] | https://openaccess.thecvf.com/content/ICCV2021/html/Chen_Unsupervised_Learning_of_Fine_Structure_Generation_for_3D_Point_Clouds_ICCV_2021_paper.html | https://openaccess.thecvf.com/content/ICCV2021/papers/Chen_Unsupervised_Learning_of_Fine_Structure_Generation_for_3D_Point_Clouds_ICCV_2021_paper.pdf | https://openaccess.thecvf.com/content/ICCV2021/supplemental/Chen_Unsupervised_Learning_of_ICCV_2021_supplemental.pdf | 2108.03746 | cvf | @InProceedings{Chen_2021_ICCV,
author = {Chen, Chao and Han, Zhizhong and Liu, Yu-Shen and Zwicker, Matthias},
title = {Unsupervised Learning of Fine Structure Generation for 3D Point Clouds by 2D Projections Matching},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vis... | Learning to generate 3D point clouds without 3D supervision is an important but challenging problem. Current solutions leverage various differentiable renderers to project the generated 3D point clouds onto a 2D image plane, and train deep neural networks using the per-pixel difference with 2D ground truth images. Howe... |
Ahn_SS-IL_Separated_Softmax_for_Incremental_Learning_ICCV_2021_paper | SS-IL: Separated Softmax for Incremental Learning | [
"Hongjoon Ahn",
"Jihwan Kwak",
"Subin Lim",
"Hyeonsu Bang",
"Hyojun Kim",
"Taesup Moon"
] | https://openaccess.thecvf.com/content/ICCV2021/html/Ahn_SS-IL_Separated_Softmax_for_Incremental_Learning_ICCV_2021_paper.html | https://openaccess.thecvf.com/content/ICCV2021/papers/Ahn_SS-IL_Separated_Softmax_for_Incremental_Learning_ICCV_2021_paper.pdf | https://openaccess.thecvf.com/content/ICCV2021/supplemental/Ahn_SS-IL_Separated_Softmax_ICCV_2021_supplemental.pdf | 2003.13947 | title_snapshot | @InProceedings{Ahn_2021_ICCV,
author = {Ahn, Hongjoon and Kwak, Jihwan and Lim, Subin and Bang, Hyeonsu and Kim, Hyojun and Moon, Taesup},
title = {SS-IL: Separated Softmax for Incremental Learning},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
mon... | We consider class incremental learning (CIL) problem, in which a learning agent continuously learns new classes from incrementally arriving training data batches and aims to predict well on all the classes learned so far. The main challenge of the problem is the catastrophic forgetting, and for the exemplar-memory base... |
Saquil_Multiple_Pairwise_Ranking_Networks_for_Personalized_Video_Summarization_ICCV_2021_paper | Multiple Pairwise Ranking Networks for Personalized Video Summarization | [
"Yassir Saquil",
"Da Chen",
"Yuan He",
"Chuan Li",
"Yong-Liang Yang"
] | https://openaccess.thecvf.com/content/ICCV2021/html/Saquil_Multiple_Pairwise_Ranking_Networks_for_Personalized_Video_Summarization_ICCV_2021_paper.html | https://openaccess.thecvf.com/content/ICCV2021/papers/Saquil_Multiple_Pairwise_Ranking_Networks_for_Personalized_Video_Summarization_ICCV_2021_paper.pdf | https://openaccess.thecvf.com/content/ICCV2021/supplemental/Saquil_Multiple_Pairwise_Ranking_ICCV_2021_supplemental.pdf | null | null | @InProceedings{Saquil_2021_ICCV,
author = {Saquil, Yassir and Chen, Da and He, Yuan and Li, Chuan and Yang, Yong-Liang},
title = {Multiple Pairwise Ranking Networks for Personalized Video Summarization},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
... | In this paper, we investigate video summarization in the supervised setting. Since video summarization is subjective to the preference of the end-user, the design of a unique model is limited. In this work, we propose a model that provides personalized video summaries by conditioning the summarization process with pred... |
Lin_Domain-Invariant_Disentangled_Network_for_Generalizable_Object_Detection_ICCV_2021_paper | Domain-Invariant Disentangled Network for Generalizable Object Detection | [
"Chuang Lin",
"Zehuan Yuan",
"Sicheng Zhao",
"Peize Sun",
"Changhu Wang",
"Jianfei Cai"
] | https://openaccess.thecvf.com/content/ICCV2021/html/Lin_Domain-Invariant_Disentangled_Network_for_Generalizable_Object_Detection_ICCV_2021_paper.html | https://openaccess.thecvf.com/content/ICCV2021/papers/Lin_Domain-Invariant_Disentangled_Network_for_Generalizable_Object_Detection_ICCV_2021_paper.pdf | null | null | null | @InProceedings{Lin_2021_ICCV,
author = {Lin, Chuang and Yuan, Zehuan and Zhao, Sicheng and Sun, Peize and Wang, Changhu and Cai, Jianfei},
title = {Domain-Invariant Disentangled Network for Generalizable Object Detection},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer ... | We address the problem of domain generalizable object detection, which aims to learn a domain-invariant detector from multiple "seen" domains so that it can generalize well to other "unseen" domains. The generalization ability is crucial in practical scenarios especially when it is difficult to collect data. Compared t... |
Liu_Social_NCE_Contrastive_Learning_of_Socially-Aware_Motion_Representations_ICCV_2021_paper | Social NCE: Contrastive Learning of Socially-Aware Motion Representations | [
"Yuejiang Liu",
"Qi Yan",
"Alexandre Alahi"
] | https://openaccess.thecvf.com/content/ICCV2021/html/Liu_Social_NCE_Contrastive_Learning_of_Socially-Aware_Motion_Representations_ICCV_2021_paper.html | https://openaccess.thecvf.com/content/ICCV2021/papers/Liu_Social_NCE_Contrastive_Learning_of_Socially-Aware_Motion_Representations_ICCV_2021_paper.pdf | https://openaccess.thecvf.com/content/ICCV2021/supplemental/Liu_Social_NCE_Contrastive_ICCV_2021_supplemental.pdf | 2012.11717 | cvf | @InProceedings{Liu_2021_ICCV,
author = {Liu, Yuejiang and Yan, Qi and Alahi, Alexandre},
title = {Social NCE: Contrastive Learning of Socially-Aware Motion Representations},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
month = {October},
ye... | Learning socially-aware motion representations is at the core of recent advances in multi-agent problems, such as human motion forecasting and robot navigation in crowds. Despite promising progress, existing representations learned with neural networks still struggle to generalize in closed-loop predictions (e.g., outp... |
Braso_The_Center_of_Attention_Center-Keypoint_Grouping_via_Attention_for_Multi-Person_ICCV_2021_paper | The Center of Attention: Center-Keypoint Grouping via Attention for Multi-Person Pose Estimation | [
"Guillem Brasó",
"Nikita Kister",
"Laura Leal-Taixé"
] | https://openaccess.thecvf.com/content/ICCV2021/html/Braso_The_Center_of_Attention_Center-Keypoint_Grouping_via_Attention_for_Multi-Person_ICCV_2021_paper.html | https://openaccess.thecvf.com/content/ICCV2021/papers/Braso_The_Center_of_Attention_Center-Keypoint_Grouping_via_Attention_for_Multi-Person_ICCV_2021_paper.pdf | https://openaccess.thecvf.com/content/ICCV2021/supplemental/Braso_The_Center_of_ICCV_2021_supplemental.pdf | 2110.05132 | title_snapshot | @InProceedings{Braso_2021_ICCV,
author = {Bras\'o, Guillem and Kister, Nikita and Leal-Taix\'e, Laura},
title = {The Center of Attention: Center-Keypoint Grouping via Attention for Multi-Person Pose Estimation},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICC... | We introduce CenterGroup, an attention-based framework to estimate human poses from a set of identity-agnostic keypoints and person center predictions in an image. Our approach uses a transformer to obtain context-aware embeddings for all detected keypoints and centers and then applies multi-head attention to directly ... |
Cheng_FloW_A_Dataset_and_Benchmark_for_Floating_Waste_Detection_in_ICCV_2021_paper | FloW: A Dataset and Benchmark for Floating Waste Detection in Inland Waters | [
"Yuwei Cheng",
"Jiannan Zhu",
"Mengxin Jiang",
"Jie Fu",
"Changsong Pang",
"Peidong Wang",
"Kris Sankaran",
"Olawale Onabola",
"Yimin Liu",
"Dianbo Liu",
"Yoshua Bengio"
] | https://openaccess.thecvf.com/content/ICCV2021/html/Cheng_FloW_A_Dataset_and_Benchmark_for_Floating_Waste_Detection_in_ICCV_2021_paper.html | https://openaccess.thecvf.com/content/ICCV2021/papers/Cheng_FloW_A_Dataset_and_Benchmark_for_Floating_Waste_Detection_in_ICCV_2021_paper.pdf | null | null | null | @InProceedings{Cheng_2021_ICCV,
author = {Cheng, Yuwei and Zhu, Jiannan and Jiang, Mengxin and Fu, Jie and Pang, Changsong and Wang, Peidong and Sankaran, Kris and Onabola, Olawale and Liu, Yimin and Liu, Dianbo and Bengio, Yoshua},
title = {FloW: A Dataset and Benchmark for Floating Waste Detection in I... | Marine debris is severely threatening the marine lives and causing sustained pollution to the whole ecosystem. To prevent the wastes from getting into the ocean, it is helpful to clean up the floating wastes in inland waters using the autonomous cleaning devices like unmanned surface vehicles. The cleaning efficiency r... |
Marin_Robust_Trust_Region_for_Weakly_Supervised_Segmentation_ICCV_2021_paper | Robust Trust Region for Weakly Supervised Segmentation | [
"Dmitrii Marin",
"Yuri Boykov"
] | https://openaccess.thecvf.com/content/ICCV2021/html/Marin_Robust_Trust_Region_for_Weakly_Supervised_Segmentation_ICCV_2021_paper.html | https://openaccess.thecvf.com/content/ICCV2021/papers/Marin_Robust_Trust_Region_for_Weakly_Supervised_Segmentation_ICCV_2021_paper.pdf | null | 2104.01948 | cvf | @InProceedings{Marin_2021_ICCV,
author = {Marin, Dmitrii and Boykov, Yuri},
title = {Robust Trust Region for Weakly Supervised Segmentation},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
month = {October},
year = {2021},
pages ... | Acquisition of training data for the standard semantic segmentation is expensive if requiring that each pixel is labeled. Yet, current methods significantly deteriorate in weakly supervised settings, e.g. where a fraction of pixels is labeled or when only image-level tags are available. It has been shown that regulariz... |
Alldieck_imGHUM_Implicit_Generative_Models_of_3D_Human_Shape_and_Articulated_ICCV_2021_paper | imGHUM: Implicit Generative Models of 3D Human Shape and Articulated Pose | [
"Thiemo Alldieck",
"Hongyi Xu",
"Cristian Sminchisescu"
] | https://openaccess.thecvf.com/content/ICCV2021/html/Alldieck_imGHUM_Implicit_Generative_Models_of_3D_Human_Shape_and_Articulated_ICCV_2021_paper.html | https://openaccess.thecvf.com/content/ICCV2021/papers/Alldieck_imGHUM_Implicit_Generative_Models_of_3D_Human_Shape_and_Articulated_ICCV_2021_paper.pdf | https://openaccess.thecvf.com/content/ICCV2021/supplemental/Alldieck_imGHUM_Implicit_Generative_ICCV_2021_supplemental.pdf | 2108.10842 | cvf | @InProceedings{Alldieck_2021_ICCV,
author = {Alldieck, Thiemo and Xu, Hongyi and Sminchisescu, Cristian},
title = {imGHUM: Implicit Generative Models of 3D Human Shape and Articulated Pose},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
month = ... | We present imGHUM, the first holistic generative model of 3D human shape and articulated pose, represented as a signed distance function. In contrast to prior work, we model the full human body implicitly as a function zero-level-set and without the use of an explicit template mesh. We propose a novel network architect... |
Magid_Dynamic_High-Pass_Filtering_and_Multi-Spectral_Attention_for_Image_Super-Resolution_ICCV_2021_paper | Dynamic High-Pass Filtering and Multi-Spectral Attention for Image Super-Resolution | [
"Salma Abdel Magid",
"Yulun Zhang",
"Donglai Wei",
"Won-Dong Jang",
"Zudi Lin",
"Yun Fu",
"Hanspeter Pfister"
] | https://openaccess.thecvf.com/content/ICCV2021/html/Magid_Dynamic_High-Pass_Filtering_and_Multi-Spectral_Attention_for_Image_Super-Resolution_ICCV_2021_paper.html | https://openaccess.thecvf.com/content/ICCV2021/papers/Magid_Dynamic_High-Pass_Filtering_and_Multi-Spectral_Attention_for_Image_Super-Resolution_ICCV_2021_paper.pdf | null | null | null | @InProceedings{Magid_2021_ICCV,
author = {Magid, Salma Abdel and Zhang, Yulun and Wei, Donglai and Jang, Won-Dong and Lin, Zudi and Fu, Yun and Pfister, Hanspeter},
title = {Dynamic High-Pass Filtering and Multi-Spectral Attention for Image Super-Resolution},
booktitle = {Proceedings of the IEEE/CVF ... | Deep convolutional neural networks (CNNs) have pushed forward the frontier of super-resolution (SR) research. However, current CNN models exhibit a major flaw: they are biased towards learning low-frequency signals. This bias becomes more problematic for the image SR task which targets reconstructing all fine details a... |
Kim_Self-Knowledge_Distillation_With_Progressive_Refinement_of_Targets_ICCV_2021_paper | Self-Knowledge Distillation With Progressive Refinement of Targets | [
"Kyungyul Kim",
"ByeongMoon Ji",
"Doyoung Yoon",
"Sangheum Hwang"
] | https://openaccess.thecvf.com/content/ICCV2021/html/Kim_Self-Knowledge_Distillation_With_Progressive_Refinement_of_Targets_ICCV_2021_paper.html | https://openaccess.thecvf.com/content/ICCV2021/papers/Kim_Self-Knowledge_Distillation_With_Progressive_Refinement_of_Targets_ICCV_2021_paper.pdf | https://openaccess.thecvf.com/content/ICCV2021/supplemental/Kim_Self-Knowledge_Distillation_With_ICCV_2021_supplemental.pdf | 2006.12000 | cvf | @InProceedings{Kim_2021_ICCV,
author = {Kim, Kyungyul and Ji, ByeongMoon and Yoon, Doyoung and Hwang, Sangheum},
title = {Self-Knowledge Distillation With Progressive Refinement of Targets},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
month = ... | The generalization capability of deep neural networks has been substantially improved by applying a wide spectrum of regularization methods, e.g., restricting function space, injecting randomness during training, augmenting data, etc. In this work, we propose a simple yet effective regularization method named progressi... |
Jiang_Towards_Flexible_Blind_JPEG_Artifacts_Removal_ICCV_2021_paper | Towards Flexible Blind JPEG Artifacts Removal | [
"Jiaxi Jiang",
"Kai Zhang",
"Radu Timofte"
] | https://openaccess.thecvf.com/content/ICCV2021/html/Jiang_Towards_Flexible_Blind_JPEG_Artifacts_Removal_ICCV_2021_paper.html | https://openaccess.thecvf.com/content/ICCV2021/papers/Jiang_Towards_Flexible_Blind_JPEG_Artifacts_Removal_ICCV_2021_paper.pdf | null | 2109.14573 | cvf | @InProceedings{Jiang_2021_ICCV,
author = {Jiang, Jiaxi and Zhang, Kai and Timofte, Radu},
title = {Towards Flexible Blind JPEG Artifacts Removal},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
month = {October},
year = {2021},
pages... | Training a single deep blind model to handle different quality factors for JPEG image artifacts removal has been attracting considerable attention due to its convenience for practical usage. However, existing deep blind methods usually directly reconstruct the image without predicting the quality factor, thus lacking t... |
Chen_Channel-Wise_Topology_Refinement_Graph_Convolution_for_Skeleton-Based_Action_Recognition_ICCV_2021_paper | Channel-Wise Topology Refinement Graph Convolution for Skeleton-Based Action Recognition | [
"Yuxin Chen",
"Ziqi Zhang",
"Chunfeng Yuan",
"Bing Li",
"Ying Deng",
"Weiming Hu"
] | https://openaccess.thecvf.com/content/ICCV2021/html/Chen_Channel-Wise_Topology_Refinement_Graph_Convolution_for_Skeleton-Based_Action_Recognition_ICCV_2021_paper.html | https://openaccess.thecvf.com/content/ICCV2021/papers/Chen_Channel-Wise_Topology_Refinement_Graph_Convolution_for_Skeleton-Based_Action_Recognition_ICCV_2021_paper.pdf | https://openaccess.thecvf.com/content/ICCV2021/supplemental/Chen_Channel-Wise_Topology_Refinement_ICCV_2021_supplemental.pdf | 2107.12213 | cvf | @InProceedings{Chen_2021_ICCV,
author = {Chen, Yuxin and Zhang, Ziqi and Yuan, Chunfeng and Li, Bing and Deng, Ying and Hu, Weiming},
title = {Channel-Wise Topology Refinement Graph Convolution for Skeleton-Based Action Recognition},
booktitle = {Proceedings of the IEEE/CVF International Conference o... | Graph convolutional networks (GCNs) have been widely used and achieved remarkable results in skeleton-based action recognition. In GCNs, graph topology dominates feature aggregation and therefore is the key to extracting representative features. In this work, we propose a novel Channel-wise Topology Refinement Graph Co... |
Ivashechkin_VSAC_Efficient_and_Accurate_Estimator_for_H_and_F_ICCV_2021_paper | VSAC: Efficient and Accurate Estimator for H and F | [
"Maksym Ivashechkin",
"Daniel Barath",
"Jiří Matas"
] | https://openaccess.thecvf.com/content/ICCV2021/html/Ivashechkin_VSAC_Efficient_and_Accurate_Estimator_for_H_and_F_ICCV_2021_paper.html | https://openaccess.thecvf.com/content/ICCV2021/papers/Ivashechkin_VSAC_Efficient_and_Accurate_Estimator_for_H_and_F_ICCV_2021_paper.pdf | https://openaccess.thecvf.com/content/ICCV2021/supplemental/Ivashechkin_VSAC_Efficient_and_ICCV_2021_supplemental.pdf | 2106.10240 | cvf | @InProceedings{Ivashechkin_2021_ICCV,
author = {Ivashechkin, Maksym and Barath, Daniel and Matas, Ji\v{r}{\'\i}},
title = {VSAC: Efficient and Accurate Estimator for H and F},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
month = {October},
... | We present VSAC, a RANSAC-type robust estimator with a number of novelties. It benefits from the introduction of the concept of independent inliers that improves significantly the efficacy of the dominant plane handling and also allows near error-free rejection of incorrect models, without false positives. The local op... |
Yan_HPNet_Deep_Primitive_Segmentation_Using_Hybrid_Representations_ICCV_2021_paper | HPNet: Deep Primitive Segmentation Using Hybrid Representations | [
"Siming Yan",
"Zhenpei Yang",
"Chongyang Ma",
"Haibin Huang",
"Etienne Vouga",
"Qixing Huang"
] | https://openaccess.thecvf.com/content/ICCV2021/html/Yan_HPNet_Deep_Primitive_Segmentation_Using_Hybrid_Representations_ICCV_2021_paper.html | https://openaccess.thecvf.com/content/ICCV2021/papers/Yan_HPNet_Deep_Primitive_Segmentation_Using_Hybrid_Representations_ICCV_2021_paper.pdf | https://openaccess.thecvf.com/content/ICCV2021/supplemental/Yan_HPNet_Deep_Primitive_ICCV_2021_supplemental.pdf | 2105.10620 | cvf | @InProceedings{Yan_2021_ICCV,
author = {Yan, Siming and Yang, Zhenpei and Ma, Chongyang and Huang, Haibin and Vouga, Etienne and Huang, Qixing},
title = {HPNet: Deep Primitive Segmentation Using Hybrid Representations},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vis... | This paper introduces HPNet, a novel deep-learning approach for segmenting a 3D shape represented as a point cloud into primitive patches. The key to deep primitive segmentation is learning a feature representation that can separate points of different primitives. Unlike utilizing a single feature representation, HPNet... |
Hutschenreiter_Fusion_Moves_for_Graph_Matching_ICCV_2021_paper | Fusion Moves for Graph Matching | [
"Lisa Hutschenreiter",
"Stefan Haller",
"Lorenz Feineis",
"Carsten Rother",
"Dagmar Kainmüller",
"Bogdan Savchynskyy"
] | https://openaccess.thecvf.com/content/ICCV2021/html/Hutschenreiter_Fusion_Moves_for_Graph_Matching_ICCV_2021_paper.html | https://openaccess.thecvf.com/content/ICCV2021/papers/Hutschenreiter_Fusion_Moves_for_Graph_Matching_ICCV_2021_paper.pdf | https://openaccess.thecvf.com/content/ICCV2021/supplemental/Hutschenreiter_Fusion_Moves_for_ICCV_2021_supplemental.pdf | 2101.12085 | cvf | @InProceedings{Hutschenreiter_2021_ICCV,
author = {Hutschenreiter, Lisa and Haller, Stefan and Feineis, Lorenz and Rother, Carsten and Kainm\"uller, Dagmar and Savchynskyy, Bogdan},
title = {Fusion Moves for Graph Matching},
booktitle = {Proceedings of the IEEE/CVF International Conference on Compute... | We contribute to approximate algorithms for the quadratic assignment problem also known as graph matching. Inspired by the success of the fusion moves technique developed for multilabel discrete Markov random fields, we investigate its applicability to graph matching. In particular, we show how fusion moves can be effi... |
Wu_Universal-Prototype_Enhancing_for_Few-Shot_Object_Detection_ICCV_2021_paper | Universal-Prototype Enhancing for Few-Shot Object Detection | [
"Aming Wu",
"Yahong Han",
"Linchao Zhu",
"Yi Yang"
] | https://openaccess.thecvf.com/content/ICCV2021/html/Wu_Universal-Prototype_Enhancing_for_Few-Shot_Object_Detection_ICCV_2021_paper.html | https://openaccess.thecvf.com/content/ICCV2021/papers/Wu_Universal-Prototype_Enhancing_for_Few-Shot_Object_Detection_ICCV_2021_paper.pdf | https://openaccess.thecvf.com/content/ICCV2021/supplemental/Wu_Universal-Prototype_Enhancing_for_ICCV_2021_supplemental.pdf | 2103.01077 | cvf | @InProceedings{Wu_2021_ICCV,
author = {Wu, Aming and Han, Yahong and Zhu, Linchao and Yang, Yi},
title = {Universal-Prototype Enhancing for Few-Shot Object Detection},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
month = {October},
year ... | Few-shot object detection (FSOD) aims to strengthen the performance of novel object detection with few labeled samples. To alleviate the constraint of few samples, enhancing the generalization ability of learned features for novel objects plays a key role. Thus, the feature learning process of FSOD should focus more on... |
Chen_I2UV-HandNet_Image-to-UV_Prediction_Network_for_Accurate_and_High-Fidelity_3D_Hand_ICCV_2021_paper | I2UV-HandNet: Image-to-UV Prediction Network for Accurate and High-Fidelity 3D Hand Mesh Modeling | [
"Ping Chen",
"Yujin Chen",
"Dong Yang",
"Fangyin Wu",
"Qin Li",
"Qingpei Xia",
"Yong Tan"
] | https://openaccess.thecvf.com/content/ICCV2021/html/Chen_I2UV-HandNet_Image-to-UV_Prediction_Network_for_Accurate_and_High-Fidelity_3D_Hand_ICCV_2021_paper.html | https://openaccess.thecvf.com/content/ICCV2021/papers/Chen_I2UV-HandNet_Image-to-UV_Prediction_Network_for_Accurate_and_High-Fidelity_3D_Hand_ICCV_2021_paper.pdf | https://openaccess.thecvf.com/content/ICCV2021/supplemental/Chen_I2UV-HandNet_Image-to-UV_Prediction_ICCV_2021_supplemental.pdf | 2102.03725 | title_snapshot | @InProceedings{Chen_2021_ICCV,
author = {Chen, Ping and Chen, Yujin and Yang, Dong and Wu, Fangyin and Li, Qin and Xia, Qingpei and Tan, Yong},
title = {I2UV-HandNet: Image-to-UV Prediction Network for Accurate and High-Fidelity 3D Hand Mesh Modeling},
booktitle = {Proceedings of the IEEE/CVF Interna... | Reconstructing a high-precision and high-fidelity 3D human hand from a color image plays a central role in replicating a realistic virtual hand in human-computer interaction and virtual reality applications. Current methods are lacking in accuracy and fidelity due to various hand poses and severe occlusions. In this st... |
Gao_Fast_Video_Moment_Retrieval_ICCV_2021_paper | Fast Video Moment Retrieval | [
"Junyu Gao",
"Changsheng Xu"
] | https://openaccess.thecvf.com/content/ICCV2021/html/Gao_Fast_Video_Moment_Retrieval_ICCV_2021_paper.html | https://openaccess.thecvf.com/content/ICCV2021/papers/Gao_Fast_Video_Moment_Retrieval_ICCV_2021_paper.pdf | null | null | null | @InProceedings{Gao_2021_ICCV,
author = {Gao, Junyu and Xu, Changsheng},
title = {Fast Video Moment Retrieval},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
month = {October},
year = {2021},
pages = {1523-1532}
} | This paper targets at fast video moment retrieval (fast VMR), aiming to localize the target moment efficiently and accurately as queried by a given natural language sentence. We argue that most existing VMR approaches can be divided into three modules namely video encoder, text encoder, and cross-modal interaction modu... |
Li_Self-Supervised_Geometric_Features_Discovery_via_Interpretable_Attention_for_Vehicle_Re-Identification_ICCV_2021_paper | Self-Supervised Geometric Features Discovery via Interpretable Attention for Vehicle Re-Identification and Beyond | [
"Ming Li",
"Xinming Huang",
"Ziming Zhang"
] | https://openaccess.thecvf.com/content/ICCV2021/html/Li_Self-Supervised_Geometric_Features_Discovery_via_Interpretable_Attention_for_Vehicle_Re-Identification_ICCV_2021_paper.html | https://openaccess.thecvf.com/content/ICCV2021/papers/Li_Self-Supervised_Geometric_Features_Discovery_via_Interpretable_Attention_for_Vehicle_Re-Identification_ICCV_2021_paper.pdf | null | 2010.09221 | title_snapshot | @InProceedings{Li_2021_ICCV,
author = {Li, Ming and Huang, Xinming and Zhang, Ziming},
title = {Self-Supervised Geometric Features Discovery via Interpretable Attention for Vehicle Re-Identification and Beyond},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICC... | To learn distinguishable patterns, most of recent works in vehicle re-identification (ReID) struggled to redevelop official benchmarks to provide various supervisions, which requires prohibitive human labors. In this paper, we seek to achieve the similar goal but do not involve more human efforts. To this end, we intro... |
Dwibedi_With_a_Little_Help_From_My_Friends_Nearest-Neighbor_Contrastive_Learning_ICCV_2021_paper | With a Little Help From My Friends: Nearest-Neighbor Contrastive Learning of Visual Representations | [
"Debidatta Dwibedi",
"Yusuf Aytar",
"Jonathan Tompson",
"Pierre Sermanet",
"Andrew Zisserman"
] | https://openaccess.thecvf.com/content/ICCV2021/html/Dwibedi_With_a_Little_Help_From_My_Friends_Nearest-Neighbor_Contrastive_Learning_ICCV_2021_paper.html | https://openaccess.thecvf.com/content/ICCV2021/papers/Dwibedi_With_a_Little_Help_From_My_Friends_Nearest-Neighbor_Contrastive_Learning_ICCV_2021_paper.pdf | https://openaccess.thecvf.com/content/ICCV2021/supplemental/Dwibedi_With_a_Little_ICCV_2021_supplemental.pdf | 2104.14548 | cvf | @InProceedings{Dwibedi_2021_ICCV,
author = {Dwibedi, Debidatta and Aytar, Yusuf and Tompson, Jonathan and Sermanet, Pierre and Zisserman, Andrew},
title = {With a Little Help From My Friends: Nearest-Neighbor Contrastive Learning of Visual Representations},
booktitle = {Proceedings of the IEEE/CVF In... | Self-supervised learning algorithms based on instance discrimination train encoders to be invariant to pre-defined transformations of the same instance. While most methods treat different views of the same image as positives for a contrastive loss, we are interested in using positives from other instances in the datase... |
Chen_Explainable_Person_Re-Identification_With_Attribute-Guided_Metric_Distillation_ICCV_2021_paper | Explainable Person Re-Identification With Attribute-Guided Metric Distillation | [
"Xiaodong Chen",
"Xinchen Liu",
"Wu Liu",
"Xiao-Ping Zhang",
"Yongdong Zhang",
"Tao Mei"
] | https://openaccess.thecvf.com/content/ICCV2021/html/Chen_Explainable_Person_Re-Identification_With_Attribute-Guided_Metric_Distillation_ICCV_2021_paper.html | https://openaccess.thecvf.com/content/ICCV2021/papers/Chen_Explainable_Person_Re-Identification_With_Attribute-Guided_Metric_Distillation_ICCV_2021_paper.pdf | https://openaccess.thecvf.com/content/ICCV2021/supplemental/Chen_Explainable_Person_Re-Identification_ICCV_2021_supplemental.pdf | 2103.01451 | cvf | @InProceedings{Chen_2021_ICCV,
author = {Chen, Xiaodong and Liu, Xinchen and Liu, Wu and Zhang, Xiao-Ping and Zhang, Yongdong and Mei, Tao},
title = {Explainable Person Re-Identification With Attribute-Guided Metric Distillation},
booktitle = {Proceedings of the IEEE/CVF International Conference on C... | Despite the great progress of person re-identification (ReID) with the adoption of Convolutional Neural Networks, current ReID models are opaque and only outputs a scalar distance between two persons. There are few methods providing users semantically understandable explanations for why two persons are the same one or ... |
Li_Motion-Focused_Contrastive_Learning_of_Video_Representations_ICCV_2021_paper | Motion-Focused Contrastive Learning of Video Representations | [
"Rui Li",
"Yiheng Zhang",
"Zhaofan Qiu",
"Ting Yao",
"Dong Liu",
"Tao Mei"
] | https://openaccess.thecvf.com/content/ICCV2021/html/Li_Motion-Focused_Contrastive_Learning_of_Video_Representations_ICCV_2021_paper.html | https://openaccess.thecvf.com/content/ICCV2021/papers/Li_Motion-Focused_Contrastive_Learning_of_Video_Representations_ICCV_2021_paper.pdf | null | 2201.04029 | title_snapshot | @InProceedings{Li_2021_ICCV,
author = {Li, Rui and Zhang, Yiheng and Qiu, Zhaofan and Yao, Ting and Liu, Dong and Mei, Tao},
title = {Motion-Focused Contrastive Learning of Video Representations},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
month ... | Motion, as the most distinct phenomenon in a video to involve the changes over time, has been unique and critical to the development of video representation learning. In this paper, we ask the question: how important is the motion particularly for self-supervised video representation learning. To this end, we compose a... |
Chen_Motion_Guided_Region_Message_Passing_for_Video_Captioning_ICCV_2021_paper | Motion Guided Region Message Passing for Video Captioning | [
"Shaoxiang Chen",
"Yu-Gang Jiang"
] | https://openaccess.thecvf.com/content/ICCV2021/html/Chen_Motion_Guided_Region_Message_Passing_for_Video_Captioning_ICCV_2021_paper.html | https://openaccess.thecvf.com/content/ICCV2021/papers/Chen_Motion_Guided_Region_Message_Passing_for_Video_Captioning_ICCV_2021_paper.pdf | https://openaccess.thecvf.com/content/ICCV2021/supplemental/Chen_Motion_Guided_Region_ICCV_2021_supplemental.pdf | null | null | @InProceedings{Chen_2021_ICCV,
author = {Chen, Shaoxiang and Jiang, Yu-Gang},
title = {Motion Guided Region Message Passing for Video Captioning},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
month = {October},
year = {2021},
pages... | Video captioning is an important vision task and has been intensively studied in the computer vision community. Existing methods that utilize the fine-grained spatial information have achieved significant improvements, however, they either rely on costly external object detectors or do not sufficiently model the spatia... |
Zhang_Learning_Causal_Representation_for_Training_Cross-Domain_Pose_Estimator_via_Generative_ICCV_2021_paper | Learning Causal Representation for Training Cross-Domain Pose Estimator via Generative Interventions | [
"Xiheng Zhang",
"Yongkang Wong",
"Xiaofei Wu",
"Juwei Lu",
"Mohan Kankanhalli",
"Xiangdong Li",
"Weidong Geng"
] | https://openaccess.thecvf.com/content/ICCV2021/html/Zhang_Learning_Causal_Representation_for_Training_Cross-Domain_Pose_Estimator_via_Generative_ICCV_2021_paper.html | https://openaccess.thecvf.com/content/ICCV2021/papers/Zhang_Learning_Causal_Representation_for_Training_Cross-Domain_Pose_Estimator_via_Generative_ICCV_2021_paper.pdf | https://openaccess.thecvf.com/content/ICCV2021/supplemental/Zhang_Learning_Causal_Representation_ICCV_2021_supplemental.pdf | null | null | @InProceedings{Zhang_2021_ICCV,
author = {Zhang, Xiheng and Wong, Yongkang and Wu, Xiaofei and Lu, Juwei and Kankanhalli, Mohan and Li, Xiangdong and Geng, Weidong},
title = {Learning Causal Representation for Training Cross-Domain Pose Estimator via Generative Interventions},
booktitle = {Proceeding... | 3D pose estimation has attracted increasing attention with the availability of high-quality benchmark datasets. However, prior works show that deep learning models tend to learn spurious correlations, which fail to generalize beyond the specific dataset they are trained on. In this work, we take a step towards training... |
Li_Super-Resolving_Cross-Domain_Face_Miniatures_by_Peeking_at_One-Shot_Exemplar_ICCV_2021_paper | Super-Resolving Cross-Domain Face Miniatures by Peeking at One-Shot Exemplar | [
"Peike Li",
"Xin Yu",
"Yi Yang"
] | https://openaccess.thecvf.com/content/ICCV2021/html/Li_Super-Resolving_Cross-Domain_Face_Miniatures_by_Peeking_at_One-Shot_Exemplar_ICCV_2021_paper.html | https://openaccess.thecvf.com/content/ICCV2021/papers/Li_Super-Resolving_Cross-Domain_Face_Miniatures_by_Peeking_at_One-Shot_Exemplar_ICCV_2021_paper.pdf | null | 2103.08863 | cvf | @InProceedings{Li_2021_ICCV,
author = {Li, Peike and Yu, Xin and Yang, Yi},
title = {Super-Resolving Cross-Domain Face Miniatures by Peeking at One-Shot Exemplar},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
month = {October},
year = ... | Conventional face super-resolution methods usually assume testing low-resolution (LR) images lie in the same domain as the training ones. Due to different lighting conditions and imaging hardware, domain gaps between training and testing images inevitably occur in many real-world scenarios. Neglecting those domain gaps... |
Sun_Webly_Supervised_Fine-Grained_Recognition_Benchmark_Datasets_and_an_Approach_ICCV_2021_paper | Webly Supervised Fine-Grained Recognition: Benchmark Datasets and an Approach | [
"Zeren Sun",
"Yazhou Yao",
"Xiu-Shen Wei",
"Yongshun Zhang",
"Fumin Shen",
"Jianxin Wu",
"Jian Zhang",
"Heng Tao Shen"
] | https://openaccess.thecvf.com/content/ICCV2021/html/Sun_Webly_Supervised_Fine-Grained_Recognition_Benchmark_Datasets_and_an_Approach_ICCV_2021_paper.html | https://openaccess.thecvf.com/content/ICCV2021/papers/Sun_Webly_Supervised_Fine-Grained_Recognition_Benchmark_Datasets_and_an_Approach_ICCV_2021_paper.pdf | null | 2108.02399 | cvf | @InProceedings{Sun_2021_ICCV,
author = {Sun, Zeren and Yao, Yazhou and Wei, Xiu-Shen and Zhang, Yongshun and Shen, Fumin and Wu, Jianxin and Zhang, Jian and Shen, Heng Tao},
title = {Webly Supervised Fine-Grained Recognition: Benchmark Datasets and an Approach},
booktitle = {Proceedings of the IEEE/C... | Learning from the web can ease the extreme dependence of deep learning on large-scale manually labeled datasets. Especially for fine-grained recognition, which targets at distinguishing subordinate categories, it will significantly reduce the labeling costs by leveraging free web data. Despite its significant practical... |
You_Towards_Interpretable_Deep_Networks_for_Monocular_Depth_Estimation_ICCV_2021_paper | Towards Interpretable Deep Networks for Monocular Depth Estimation | [
"Zunzhi You",
"Yi-Hsuan Tsai",
"Wei-Chen Chiu",
"Guanbin Li"
] | https://openaccess.thecvf.com/content/ICCV2021/html/You_Towards_Interpretable_Deep_Networks_for_Monocular_Depth_Estimation_ICCV_2021_paper.html | https://openaccess.thecvf.com/content/ICCV2021/papers/You_Towards_Interpretable_Deep_Networks_for_Monocular_Depth_Estimation_ICCV_2021_paper.pdf | https://openaccess.thecvf.com/content/ICCV2021/supplemental/You_Towards_Interpretable_Deep_ICCV_2021_supplemental.pdf | 2108.05312 | cvf | @InProceedings{You_2021_ICCV,
author = {You, Zunzhi and Tsai, Yi-Hsuan and Chiu, Wei-Chen and Li, Guanbin},
title = {Towards Interpretable Deep Networks for Monocular Depth Estimation},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
month = {Octo... | Deep networks for Monocular Depth Estimation (MDE) have achieved promising performance recently and it is of great importance to further understand the interpretability of these networks. Existing methods attempt to provide post-hoc explanations by investigating visual cues, which may not explore the internal represent... |
Liang_Instance_Segmentation_in_3D_Scenes_Using_Semantic_Superpoint_Tree_Networks_ICCV_2021_paper | Instance Segmentation in 3D Scenes Using Semantic Superpoint Tree Networks | [
"Zhihao Liang",
"Zhihao Li",
"Songcen Xu",
"Mingkui Tan",
"Kui Jia"
] | https://openaccess.thecvf.com/content/ICCV2021/html/Liang_Instance_Segmentation_in_3D_Scenes_Using_Semantic_Superpoint_Tree_Networks_ICCV_2021_paper.html | https://openaccess.thecvf.com/content/ICCV2021/papers/Liang_Instance_Segmentation_in_3D_Scenes_Using_Semantic_Superpoint_Tree_Networks_ICCV_2021_paper.pdf | https://openaccess.thecvf.com/content/ICCV2021/supplemental/Liang_Instance_Segmentation_in_ICCV_2021_supplemental.pdf | 2108.07478 | cvf | @InProceedings{Liang_2021_ICCV,
author = {Liang, Zhihao and Li, Zhihao and Xu, Songcen and Tan, Mingkui and Jia, Kui},
title = {Instance Segmentation in 3D Scenes Using Semantic Superpoint Tree Networks},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
... | Instance segmentation in 3D scenes is fundamental in many applications of scene understanding. It is yet challenging due to the compound factors of data irregularity and uncertainty in the numbers of instances. State-of-the-art methods largely rely on a general pipeline that first learns point-wise features discriminat... |
Wang_Exploring_Cross-Image_Pixel_Contrast_for_Semantic_Segmentation_ICCV_2021_paper | Exploring Cross-Image Pixel Contrast for Semantic Segmentation | [
"Wenguan Wang",
"Tianfei Zhou",
"Fisher Yu",
"Jifeng Dai",
"Ender Konukoglu",
"Luc Van Gool"
] | https://openaccess.thecvf.com/content/ICCV2021/html/Wang_Exploring_Cross-Image_Pixel_Contrast_for_Semantic_Segmentation_ICCV_2021_paper.html | https://openaccess.thecvf.com/content/ICCV2021/papers/Wang_Exploring_Cross-Image_Pixel_Contrast_for_Semantic_Segmentation_ICCV_2021_paper.pdf | https://openaccess.thecvf.com/content/ICCV2021/supplemental/Wang_Exploring_Cross-Image_Pixel_ICCV_2021_supplemental.pdf | 2101.11939 | cvf | @InProceedings{Wang_2021_ICCV,
author = {Wang, Wenguan and Zhou, Tianfei and Yu, Fisher and Dai, Jifeng and Konukoglu, Ender and Van Gool, Luc},
title = {Exploring Cross-Image Pixel Contrast for Semantic Segmentation},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Visi... | Current semantic segmentation methods focus only on mining "local" context, i.e., dependencies between pixels within individual images, by context-aggregation modules (e.g., dilated convolution, neural attention) or structure-aware optimization criteria (e.g., IoU-like loss). However, they ignore "global" context of th... |
Shi_Geometric_Granularity_Aware_Pixel-To-Mesh_ICCV_2021_paper | Geometric Granularity Aware Pixel-To-Mesh | [
"Yue Shi",
"Bingbing Ni",
"Jinxian Liu",
"Dingyi Rong",
"Ye Qian",
"Wenjun Zhang"
] | https://openaccess.thecvf.com/content/ICCV2021/html/Shi_Geometric_Granularity_Aware_Pixel-To-Mesh_ICCV_2021_paper.html | https://openaccess.thecvf.com/content/ICCV2021/papers/Shi_Geometric_Granularity_Aware_Pixel-To-Mesh_ICCV_2021_paper.pdf | https://openaccess.thecvf.com/content/ICCV2021/supplemental/Shi_Geometric_Granularity_Aware_ICCV_2021_supplemental.zip | null | null | @InProceedings{Shi_2021_ICCV,
author = {Shi, Yue and Ni, Bingbing and Liu, Jinxian and Rong, Dingyi and Qian, Ye and Zhang, Wenjun},
title = {Geometric Granularity Aware Pixel-To-Mesh},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
month = {Octo... | Pixel-to-mesh has wide applications, especially in virtual or augmented reality, animation and game industry. However, existing mesh reconstruction models perform unsatisfactorily in local geometry details due to ignoring mesh topology information during learning. Besides, most methods are constrained by the initial te... |
Su_Pixel_Difference_Networks_for_Efficient_Edge_Detection_ICCV_2021_paper | Pixel Difference Networks for Efficient Edge Detection | [
"Zhuo Su",
"Wenzhe Liu",
"Zitong Yu",
"Dewen Hu",
"Qing Liao",
"Qi Tian",
"Matti Pietikäinen",
"Li Liu"
] | https://openaccess.thecvf.com/content/ICCV2021/html/Su_Pixel_Difference_Networks_for_Efficient_Edge_Detection_ICCV_2021_paper.html | https://openaccess.thecvf.com/content/ICCV2021/papers/Su_Pixel_Difference_Networks_for_Efficient_Edge_Detection_ICCV_2021_paper.pdf | https://openaccess.thecvf.com/content/ICCV2021/supplemental/Su_Pixel_Difference_Networks_ICCV_2021_supplemental.pdf | 2108.07009 | cvf | @InProceedings{Su_2021_ICCV,
author = {Su, Zhuo and Liu, Wenzhe and Yu, Zitong and Hu, Dewen and Liao, Qing and Tian, Qi and Pietik\"ainen, Matti and Liu, Li},
title = {Pixel Difference Networks for Efficient Edge Detection},
booktitle = {Proceedings of the IEEE/CVF International Conference on Comput... | Recently, deep Convolutional Neural Networks (CNNs) can achieve human-level performance in edge detection with the rich and abstract edge representation capacities. However, the high performance of CNN based edge detection is achieved with a large pretrained CNN backbone, which is memory and energy consuming. In additi... |
Zhu_Towards_Understanding_the_Generative_Capability_of_Adversarially_Robust_Classifiers_ICCV_2021_paper | Towards Understanding the Generative Capability of Adversarially Robust Classifiers | [
"Yao Zhu",
"Jiacheng Ma",
"Jiacheng Sun",
"Zewei Chen",
"Rongxin Jiang",
"Yaowu Chen",
"Zhenguo Li"
] | https://openaccess.thecvf.com/content/ICCV2021/html/Zhu_Towards_Understanding_the_Generative_Capability_of_Adversarially_Robust_Classifiers_ICCV_2021_paper.html | https://openaccess.thecvf.com/content/ICCV2021/papers/Zhu_Towards_Understanding_the_Generative_Capability_of_Adversarially_Robust_Classifiers_ICCV_2021_paper.pdf | https://openaccess.thecvf.com/content/ICCV2021/supplemental/Zhu_Towards_Understanding_the_ICCV_2021_supplemental.pdf | 2108.09093 | cvf | @InProceedings{Zhu_2021_ICCV,
author = {Zhu, Yao and Ma, Jiacheng and Sun, Jiacheng and Chen, Zewei and Jiang, Rongxin and Chen, Yaowu and Li, Zhenguo},
title = {Towards Understanding the Generative Capability of Adversarially Robust Classifiers},
booktitle = {Proceedings of the IEEE/CVF Internationa... | Recently, some works found an interesting phenomenon that adversarially robust classifiers can generate good images comparable to generative models. We investigate this phenomenon from an energy perspective and provide a novel explanation. We reformulate adversarial example generation, adversarial training, and image g... |
Kang_Learning_Efficient_Photometric_Feature_Transform_for_Multi-View_Stereo_ICCV_2021_paper | Learning Efficient Photometric Feature Transform for Multi-View Stereo | [
"Kaizhang Kang",
"Cihui Xie",
"Ruisheng Zhu",
"Xiaohe Ma",
"Ping Tan",
"Hongzhi Wu",
"Kun Zhou"
] | https://openaccess.thecvf.com/content/ICCV2021/html/Kang_Learning_Efficient_Photometric_Feature_Transform_for_Multi-View_Stereo_ICCV_2021_paper.html | https://openaccess.thecvf.com/content/ICCV2021/papers/Kang_Learning_Efficient_Photometric_Feature_Transform_for_Multi-View_Stereo_ICCV_2021_paper.pdf | null | 2103.14794 | cvf | @InProceedings{Kang_2021_ICCV,
author = {Kang, Kaizhang and Xie, Cihui and Zhu, Ruisheng and Ma, Xiaohe and Tan, Ping and Wu, Hongzhi and Zhou, Kun},
title = {Learning Efficient Photometric Feature Transform for Multi-View Stereo},
booktitle = {Proceedings of the IEEE/CVF International Conference on ... | We present a novel framework to learn to convert the per-pixel photometric information at each view into spatially distinctive and view-invariant low-level features, which can be plugged into existing multi-view stereo pipeline for enhanced 3D reconstruction. Both the illumination conditions during acquisition and the ... |
Chitta_NEAT_Neural_Attention_Fields_for_End-to-End_Autonomous_Driving_ICCV_2021_paper | NEAT: Neural Attention Fields for End-to-End Autonomous Driving | [
"Kashyap Chitta",
"Aditya Prakash",
"Andreas Geiger"
] | https://openaccess.thecvf.com/content/ICCV2021/html/Chitta_NEAT_Neural_Attention_Fields_for_End-to-End_Autonomous_Driving_ICCV_2021_paper.html | https://openaccess.thecvf.com/content/ICCV2021/papers/Chitta_NEAT_Neural_Attention_Fields_for_End-to-End_Autonomous_Driving_ICCV_2021_paper.pdf | null | 2109.04456 | cvf | @InProceedings{Chitta_2021_ICCV,
author = {Chitta, Kashyap and Prakash, Aditya and Geiger, Andreas},
title = {NEAT: Neural Attention Fields for End-to-End Autonomous Driving},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
month = {October},
... | Efficient reasoning about the semantic, spatial, and temporal structure of a scene is a crucial prerequisite for autonomous driving. We present NEural ATtention fields (NEAT), a novel representation that enables such reasoning for end-to-end imitation learning models. NEAT is a continuous function which maps locations ... |
Mehta_Modulated_Periodic_Activations_for_Generalizable_Local_Functional_Representations_ICCV_2021_paper | Modulated Periodic Activations for Generalizable Local Functional Representations | [
"Ishit Mehta",
"Michaël Gharbi",
"Connelly Barnes",
"Eli Shechtman",
"Ravi Ramamoorthi",
"Manmohan Chandraker"
] | https://openaccess.thecvf.com/content/ICCV2021/html/Mehta_Modulated_Periodic_Activations_for_Generalizable_Local_Functional_Representations_ICCV_2021_paper.html | https://openaccess.thecvf.com/content/ICCV2021/papers/Mehta_Modulated_Periodic_Activations_for_Generalizable_Local_Functional_Representations_ICCV_2021_paper.pdf | null | 2104.03960 | cvf | @InProceedings{Mehta_2021_ICCV,
author = {Mehta, Ishit and Gharbi, Micha\"el and Barnes, Connelly and Shechtman, Eli and Ramamoorthi, Ravi and Chandraker, Manmohan},
title = {Modulated Periodic Activations for Generalizable Local Functional Representations},
booktitle = {Proceedings of the IEEE/CVF I... | Multi-Layer Perceptrons (MLPs) make powerful functional representations for sampling and reconstruction problems involving low-dimensional signals like images,shapes and light fields. Recent works have significantly improved their ability to represent high-frequency content by using periodic activations or positional e... |
Zeng_Neural_Architecture_Search_for_Joint_Human_Parsing_and_Pose_Estimation_ICCV_2021_paper | Neural Architecture Search for Joint Human Parsing and Pose Estimation | [
"Dan Zeng",
"Yuhang Huang",
"Qian Bao",
"Junjie Zhang",
"Chi Su",
"Wu Liu"
] | https://openaccess.thecvf.com/content/ICCV2021/html/Zeng_Neural_Architecture_Search_for_Joint_Human_Parsing_and_Pose_Estimation_ICCV_2021_paper.html | https://openaccess.thecvf.com/content/ICCV2021/papers/Zeng_Neural_Architecture_Search_for_Joint_Human_Parsing_and_Pose_Estimation_ICCV_2021_paper.pdf | https://openaccess.thecvf.com/content/ICCV2021/supplemental/Zeng_Neural_Architecture_Search_ICCV_2021_supplemental.pdf | null | null | @InProceedings{Zeng_2021_ICCV,
author = {Zeng, Dan and Huang, Yuhang and Bao, Qian and Zhang, Junjie and Su, Chi and Liu, Wu},
title = {Neural Architecture Search for Joint Human Parsing and Pose Estimation},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)}... | Human parsing and pose estimation are crucial for the understanding of human behaviors. Since these tasks are closely related, employing one unified model to perform two tasks simultaneously allows them to benefit from each other. However, since human parsing is a pixel-wise classification process while pose estimation... |
Huang_Fast_Light-Field_Disparity_Estimation_With_Multi-Disparity-Scale_Cost_Aggregation_ICCV_2021_paper | Fast Light-Field Disparity Estimation With Multi-Disparity-Scale Cost Aggregation | [
"Zhicong Huang",
"Xuemei Hu",
"Zhou Xue",
"Weizhu Xu",
"Tao Yue"
] | https://openaccess.thecvf.com/content/ICCV2021/html/Huang_Fast_Light-Field_Disparity_Estimation_With_Multi-Disparity-Scale_Cost_Aggregation_ICCV_2021_paper.html | https://openaccess.thecvf.com/content/ICCV2021/papers/Huang_Fast_Light-Field_Disparity_Estimation_With_Multi-Disparity-Scale_Cost_Aggregation_ICCV_2021_paper.pdf | https://openaccess.thecvf.com/content/ICCV2021/supplemental/Huang_Fast_Light-Field_Disparity_ICCV_2021_supplemental.pdf | null | null | @InProceedings{Huang_2021_ICCV,
author = {Huang, Zhicong and Hu, Xuemei and Xue, Zhou and Xu, Weizhu and Yue, Tao},
title = {Fast Light-Field Disparity Estimation With Multi-Disparity-Scale Cost Aggregation},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)}... | Light field images contain both angular and spatial information of captured light rays. The rich information of light fields enables straightforward disparity recovery capability but demands high computational cost as well. In this paper, we design a lightweight disparity estimation model with physical-based multi-disp... |
Khurana_SemIE_Semantically-Aware_Image_Extrapolation_ICCV_2021_paper | SemIE: Semantically-Aware Image Extrapolation | [
"Bholeshwar Khurana",
"Soumya Ranjan Dash",
"Abhishek Bhatia",
"Aniruddha Mahapatra",
"Hrituraj Singh",
"Kuldeep Kulkarni"
] | https://openaccess.thecvf.com/content/ICCV2021/html/Khurana_SemIE_Semantically-Aware_Image_Extrapolation_ICCV_2021_paper.html | https://openaccess.thecvf.com/content/ICCV2021/papers/Khurana_SemIE_Semantically-Aware_Image_Extrapolation_ICCV_2021_paper.pdf | https://openaccess.thecvf.com/content/ICCV2021/supplemental/Khurana_SemIE_Semantically-Aware_Image_ICCV_2021_supplemental.zip | 2108.13702 | cvf | @InProceedings{Khurana_2021_ICCV,
author = {Khurana, Bholeshwar and Dash, Soumya Ranjan and Bhatia, Abhishek and Mahapatra, Aniruddha and Singh, Hrituraj and Kulkarni, Kuldeep},
title = {SemIE: Semantically-Aware Image Extrapolation},
booktitle = {Proceedings of the IEEE/CVF International Conference ... | We propose a semantically-aware novel paradigm to perform image extrapolation that enables the addition of new object instances. All previous methods are limited in their capability of extrapolation to merely extending the already existing objects in the image. However, our proposed approach focuses not only on (i) ext... |
Zhao_Transformer-Based_Dual_Relation_Graph_for_Multi-Label_Image_Recognition_ICCV_2021_paper | Transformer-Based Dual Relation Graph for Multi-Label Image Recognition | [
"Jiawei Zhao",
"Ke Yan",
"Yifan Zhao",
"Xiaowei Guo",
"Feiyue Huang",
"Jia Li"
] | https://openaccess.thecvf.com/content/ICCV2021/html/Zhao_Transformer-Based_Dual_Relation_Graph_for_Multi-Label_Image_Recognition_ICCV_2021_paper.html | https://openaccess.thecvf.com/content/ICCV2021/papers/Zhao_Transformer-Based_Dual_Relation_Graph_for_Multi-Label_Image_Recognition_ICCV_2021_paper.pdf | https://openaccess.thecvf.com/content/ICCV2021/supplemental/Zhao_Transformer-Based_Dual_Relation_ICCV_2021_supplemental.pdf | 2110.04722 | cvf | @InProceedings{Zhao_2021_ICCV,
author = {Zhao, Jiawei and Yan, Ke and Zhao, Yifan and Guo, Xiaowei and Huang, Feiyue and Li, Jia},
title = {Transformer-Based Dual Relation Graph for Multi-Label Image Recognition},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (I... | The simultaneous recognition of multiple objects in one image remains a challenging task, spanning multiple events in the recognition field such as various object scales, inconsistent appearances, and confused inter-class relationships. Recent research efforts mainly resort to the statistic label co-occurrences and lin... |
Chen_Self-Supervised_Transfer_Learning_for_Hand_Mesh_Recovery_From_Binocular_Images_ICCV_2021_paper | Self-Supervised Transfer Learning for Hand Mesh Recovery From Binocular Images | [
"Zheng Chen",
"Sihan Wang",
"Yi Sun",
"Xiaohong Ma"
] | https://openaccess.thecvf.com/content/ICCV2021/html/Chen_Self-Supervised_Transfer_Learning_for_Hand_Mesh_Recovery_From_Binocular_Images_ICCV_2021_paper.html | https://openaccess.thecvf.com/content/ICCV2021/papers/Chen_Self-Supervised_Transfer_Learning_for_Hand_Mesh_Recovery_From_Binocular_Images_ICCV_2021_paper.pdf | https://openaccess.thecvf.com/content/ICCV2021/supplemental/Chen_Self-Supervised_Transfer_Learning_ICCV_2021_supplemental.zip | null | null | @InProceedings{Chen_2021_ICCV,
author = {Chen, Zheng and Wang, Sihan and Sun, Yi and Ma, Xiaohong},
title = {Self-Supervised Transfer Learning for Hand Mesh Recovery From Binocular Images},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
month = {... | Traditional methods for RGB hand mesh recovery usually need to train a separate model for each dataset with the corresponding ground truth and are hardly adapted to new scenarios without the ground truth for supervision. To address the problem, we propose a self-supervised framework for hand mesh estimation, where we p... |
Jeppesen_Faster_Multi-Object_Segmentation_Using_Parallel_Quadratic_Pseudo-Boolean_Optimization_ICCV_2021_paper | Faster Multi-Object Segmentation Using Parallel Quadratic Pseudo-Boolean Optimization | [
"Niels Jeppesen",
"Patrick M. Jensen",
"Anders N. Christensen",
"Anders B. Dahl",
"Vedrana A. Dahl"
] | https://openaccess.thecvf.com/content/ICCV2021/html/Jeppesen_Faster_Multi-Object_Segmentation_Using_Parallel_Quadratic_Pseudo-Boolean_Optimization_ICCV_2021_paper.html | https://openaccess.thecvf.com/content/ICCV2021/papers/Jeppesen_Faster_Multi-Object_Segmentation_Using_Parallel_Quadratic_Pseudo-Boolean_Optimization_ICCV_2021_paper.pdf | https://openaccess.thecvf.com/content/ICCV2021/supplemental/Jeppesen_Faster_Multi-Object_Segmentation_ICCV_2021_supplemental.zip | null | null | @InProceedings{Jeppesen_2021_ICCV,
author = {Jeppesen, Niels and Jensen, Patrick M. and Christensen, Anders N. and Dahl, Anders B. and Dahl, Vedrana A.},
title = {Faster Multi-Object Segmentation Using Parallel Quadratic Pseudo-Boolean Optimization},
booktitle = {Proceedings of the IEEE/CVF Internati... | We introduce a parallel version of the Quadratic Pseudo-Boolean Optimization (QPBO) algorithm for solving binary optimization tasks, such as image segmentation. The original QPBO implementation by Kolmogorov and Rother relies on the Boykov-Kolmogorov (BK) maxflow/mincut algorithm and performs well for many image analys... |
Shi_Partial_Off-Policy_Learning_Balance_Accuracy_and_Diversity_for_Human-Oriented_Image_ICCV_2021_paper | Partial Off-Policy Learning: Balance Accuracy and Diversity for Human-Oriented Image Captioning | [
"Jiahe Shi",
"Yali Li",
"Shengjin Wang"
] | https://openaccess.thecvf.com/content/ICCV2021/html/Shi_Partial_Off-Policy_Learning_Balance_Accuracy_and_Diversity_for_Human-Oriented_Image_ICCV_2021_paper.html | https://openaccess.thecvf.com/content/ICCV2021/papers/Shi_Partial_Off-Policy_Learning_Balance_Accuracy_and_Diversity_for_Human-Oriented_Image_ICCV_2021_paper.pdf | https://openaccess.thecvf.com/content/ICCV2021/supplemental/Shi_Partial_Off-Policy_Learning_ICCV_2021_supplemental.pdf | null | null | @InProceedings{Shi_2021_ICCV,
author = {Shi, Jiahe and Li, Yali and Wang, Shengjin},
title = {Partial Off-Policy Learning: Balance Accuracy and Diversity for Human-Oriented Image Captioning},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
month =... | Human-oriented image captioning with both high diversity and accuracy is a challenging task in vision+language modeling. The reinforcement learning (RL) based frameworks promote the accuracy of image captioning, yet seriously hurt the diversity. In contrast, other methods based on variational auto-encoder (VAE) or gene... |
Biertimpel_Prior_to_Segment_Foreground_Cues_for_Weakly_Annotated_Classes_in_ICCV_2021_paper | Prior to Segment: Foreground Cues for Weakly Annotated Classes in Partially Supervised Instance Segmentation | [
"David Biertimpel",
"Sindi Shkodrani",
"Anil S. Baslamisli",
"Nóra Baka"
] | https://openaccess.thecvf.com/content/ICCV2021/html/Biertimpel_Prior_to_Segment_Foreground_Cues_for_Weakly_Annotated_Classes_in_ICCV_2021_paper.html | https://openaccess.thecvf.com/content/ICCV2021/papers/Biertimpel_Prior_to_Segment_Foreground_Cues_for_Weakly_Annotated_Classes_in_ICCV_2021_paper.pdf | null | 2011.11787 | cvf | @InProceedings{Biertimpel_2021_ICCV,
author = {Biertimpel, David and Shkodrani, Sindi and Baslamisli, Anil S. and Baka, N\'ora},
title = {Prior to Segment: Foreground Cues for Weakly Annotated Classes in Partially Supervised Instance Segmentation},
booktitle = {Proceedings of the IEEE/CVF Internation... | Instance segmentation methods require large datasets with expensive and thus limited instance-level mask labels. Partially supervised instance segmentation aims to improve mask prediction with limited mask labels by utilizing the more abundant weak box labels. In this work, we show that a class agnostic mask head, comm... |
Patel_Interpretation_of_Emergent_Communication_in_Heterogeneous_Collaborative_Embodied_Agents_ICCV_2021_paper | Interpretation of Emergent Communication in Heterogeneous Collaborative Embodied Agents | [
"Shivansh Patel",
"Saim Wani",
"Unnat Jain",
"Alexander G. Schwing",
"Svetlana Lazebnik",
"Manolis Savva",
"Angel X. Chang"
] | https://openaccess.thecvf.com/content/ICCV2021/html/Patel_Interpretation_of_Emergent_Communication_in_Heterogeneous_Collaborative_Embodied_Agents_ICCV_2021_paper.html | https://openaccess.thecvf.com/content/ICCV2021/papers/Patel_Interpretation_of_Emergent_Communication_in_Heterogeneous_Collaborative_Embodied_Agents_ICCV_2021_paper.pdf | https://openaccess.thecvf.com/content/ICCV2021/supplemental/Patel_Interpretation_of_Emergent_ICCV_2021_supplemental.pdf | 2110.05769 | cvf | @InProceedings{Patel_2021_ICCV,
author = {Patel, Shivansh and Wani, Saim and Jain, Unnat and Schwing, Alexander G. and Lazebnik, Svetlana and Savva, Manolis and Chang, Angel X.},
title = {Interpretation of Emergent Communication in Heterogeneous Collaborative Embodied Agents},
booktitle = {Proceeding... | Communication between embodied AI agents has received increasing attention in recent years. Despite its use, it is still unclear whether the learned communication is interpretable and grounded in perception. To study the grounding of emergent forms of communication, we first introduce the collaborative multi-object nav... |
Xia_A_Dark_Flash_Normal_Camera_ICCV_2021_paper | A Dark Flash Normal Camera | [
"Zhihao Xia",
"Jason Lawrence",
"Supreeth Achar"
] | https://openaccess.thecvf.com/content/ICCV2021/html/Xia_A_Dark_Flash_Normal_Camera_ICCV_2021_paper.html | https://openaccess.thecvf.com/content/ICCV2021/papers/Xia_A_Dark_Flash_Normal_Camera_ICCV_2021_paper.pdf | https://openaccess.thecvf.com/content/ICCV2021/supplemental/Xia_A_Dark_Flash_ICCV_2021_supplemental.pdf | 2012.06125 | cvf | @InProceedings{Xia_2021_ICCV,
author = {Xia, Zhihao and Lawrence, Jason and Achar, Supreeth},
title = {A Dark Flash Normal Camera},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
month = {October},
year = {2021},
pages = {2430-24... | Casual photography is often performed in uncontrolled lighting that can result in low quality images and degrade the performance of downstream processing. We consider the problem of estimating surface normal and reflectance maps of scenes depicting people despite these conditions by supplementing the available visible ... |
Reed_Dynamic_CT_Reconstruction_From_Limited_Views_With_Implicit_Neural_Representations_ICCV_2021_paper | Dynamic CT Reconstruction From Limited Views With Implicit Neural Representations and Parametric Motion Fields | [
"Albert W. Reed",
"Hyojin Kim",
"Rushil Anirudh",
"K. Aditya Mohan",
"Kyle Champley",
"Jingu Kang",
"Suren Jayasuriya"
] | https://openaccess.thecvf.com/content/ICCV2021/html/Reed_Dynamic_CT_Reconstruction_From_Limited_Views_With_Implicit_Neural_Representations_ICCV_2021_paper.html | https://openaccess.thecvf.com/content/ICCV2021/papers/Reed_Dynamic_CT_Reconstruction_From_Limited_Views_With_Implicit_Neural_Representations_ICCV_2021_paper.pdf | https://openaccess.thecvf.com/content/ICCV2021/supplemental/Reed_Dynamic_CT_Reconstruction_ICCV_2021_supplemental.zip | 2104.11745 | cvf | @InProceedings{Reed_2021_ICCV,
author = {Reed, Albert W. and Kim, Hyojin and Anirudh, Rushil and Mohan, K. Aditya and Champley, Kyle and Kang, Jingu and Jayasuriya, Suren},
title = {Dynamic CT Reconstruction From Limited Views With Implicit Neural Representations and Parametric Motion Fields},
bookti... | Reconstructing dynamic, time-varying scenes with computed tomography (4D-CT) is a challenging and ill-posed problem common to industrial and medical settings. Existing 4D-CT reconstructions are designed for sparse sampling schemes that require fast CT scanners to capture multiple, rapid revolutions around the scene in ... |
Chen_Diverse_Image_Style_Transfer_via_Invertible_Cross-Space_Mapping_ICCV_2021_paper | Diverse Image Style Transfer via Invertible Cross-Space Mapping | [
"Haibo Chen",
"Lei Zhao",
"Huiming Zhang",
"Zhizhong Wang",
"Zhiwen Zuo",
"Ailin Li",
"Wei Xing",
"Dongming Lu"
] | https://openaccess.thecvf.com/content/ICCV2021/html/Chen_Diverse_Image_Style_Transfer_via_Invertible_Cross-Space_Mapping_ICCV_2021_paper.html | https://openaccess.thecvf.com/content/ICCV2021/papers/Chen_Diverse_Image_Style_Transfer_via_Invertible_Cross-Space_Mapping_ICCV_2021_paper.pdf | https://openaccess.thecvf.com/content/ICCV2021/supplemental/Chen_Diverse_Image_Style_ICCV_2021_supplemental.pdf | null | null | @InProceedings{Chen_2021_ICCV,
author = {Chen, Haibo and Zhao, Lei and Zhang, Huiming and Wang, Zhizhong and Zuo, Zhiwen and Li, Ailin and Xing, Wei and Lu, Dongming},
title = {Diverse Image Style Transfer via Invertible Cross-Space Mapping},
booktitle = {Proceedings of the IEEE/CVF International Con... | Image style transfer aims to transfer the styles of artworks onto arbitrary photographs to create novel artistic images. Although style transfer is inherently an underdetermined problem, existing approaches usually assume a deterministic solution, thus failing to capture the full distribution of possible outputs. To ad... |
Chen_Variational_Attention_Propagating_Domain-Specific_Knowledge_for_Multi-Domain_Learning_in_Crowd_ICCV_2021_paper | Variational Attention: Propagating Domain-Specific Knowledge for Multi-Domain Learning in Crowd Counting | [
"Binghui Chen",
"Zhaoyi Yan",
"Ke Li",
"Pengyu Li",
"Biao Wang",
"Wangmeng Zuo",
"Lei Zhang"
] | https://openaccess.thecvf.com/content/ICCV2021/html/Chen_Variational_Attention_Propagating_Domain-Specific_Knowledge_for_Multi-Domain_Learning_in_Crowd_ICCV_2021_paper.html | https://openaccess.thecvf.com/content/ICCV2021/papers/Chen_Variational_Attention_Propagating_Domain-Specific_Knowledge_for_Multi-Domain_Learning_in_Crowd_ICCV_2021_paper.pdf | null | 2108.08023 | cvf | @InProceedings{Chen_2021_ICCV,
author = {Chen, Binghui and Yan, Zhaoyi and Li, Ke and Li, Pengyu and Wang, Biao and Zuo, Wangmeng and Zhang, Lei},
title = {Variational Attention: Propagating Domain-Specific Knowledge for Multi-Domain Learning in Crowd Counting},
booktitle = {Proceedings of the IEEE/C... | In crowd counting, due to the problem of laborious labelling, it is perceived intractability of collecting a new large-scale dataset which has plentiful images with large diversity in density, scene, etc. Thus, for learning a general model, training with data from multiple different datasets might be a remedy and be of... |
Hou_Pri3D_Can_3D_Priors_Help_2D_Representation_Learning_ICCV_2021_paper | Pri3D: Can 3D Priors Help 2D Representation Learning? | [
"Ji Hou",
"Saining Xie",
"Benjamin Graham",
"Angela Dai",
"Matthias Nießner"
] | https://openaccess.thecvf.com/content/ICCV2021/html/Hou_Pri3D_Can_3D_Priors_Help_2D_Representation_Learning_ICCV_2021_paper.html | https://openaccess.thecvf.com/content/ICCV2021/papers/Hou_Pri3D_Can_3D_Priors_Help_2D_Representation_Learning_ICCV_2021_paper.pdf | https://openaccess.thecvf.com/content/ICCV2021/supplemental/Hou_Pri3D_Can_3D_ICCV_2021_supplemental.pdf | 2104.11225 | cvf | @InProceedings{Hou_2021_ICCV,
author = {Hou, Ji and Xie, Saining and Graham, Benjamin and Dai, Angela and Nie{\ss}ner, Matthias},
title = {Pri3D: Can 3D Priors Help 2D Representation Learning?},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
month ... | Recent advances in 3D perception have shown impressive progress in understanding geometric structures of 3D shapes and even scenes. Inspired by these advances in geometric understanding, we aim to imbue image-based perception with representations learned under geometric constraints. We introduce an approach to learn vi... |
Li_PoGO-Net_Pose_Graph_Optimization_With_Graph_Neural_Networks_ICCV_2021_paper | PoGO-Net: Pose Graph Optimization With Graph Neural Networks | [
"Xinyi Li",
"Haibin Ling"
] | https://openaccess.thecvf.com/content/ICCV2021/html/Li_PoGO-Net_Pose_Graph_Optimization_With_Graph_Neural_Networks_ICCV_2021_paper.html | https://openaccess.thecvf.com/content/ICCV2021/papers/Li_PoGO-Net_Pose_Graph_Optimization_With_Graph_Neural_Networks_ICCV_2021_paper.pdf | https://openaccess.thecvf.com/content/ICCV2021/supplemental/Li_PoGO-Net_Pose_Graph_ICCV_2021_supplemental.pdf | null | null | @InProceedings{Li_2021_ICCV,
author = {Li, Xinyi and Ling, Haibin},
title = {PoGO-Net: Pose Graph Optimization With Graph Neural Networks},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
month = {October},
year = {2021},
pages = ... | Accurate camera pose estimation or global camera re-localization is a core component in Structure-from-Motion (SfM) and SLAM systems. Given pair-wise relative camera poses, pose-graph optimization (PGO) involves solving for an optimized set of globally-consistent absolute camera poses. In this work, we propose a novel ... |
Zhang_Federated_Learning_for_Non-IID_Data_via_Unified_Feature_Learning_and_ICCV_2021_paper | Federated Learning for Non-IID Data via Unified Feature Learning and Optimization Objective Alignment | [
"Lin Zhang",
"Yong Luo",
"Yan Bai",
"Bo Du",
"Ling-Yu Duan"
] | https://openaccess.thecvf.com/content/ICCV2021/html/Zhang_Federated_Learning_for_Non-IID_Data_via_Unified_Feature_Learning_and_ICCV_2021_paper.html | https://openaccess.thecvf.com/content/ICCV2021/papers/Zhang_Federated_Learning_for_Non-IID_Data_via_Unified_Feature_Learning_and_ICCV_2021_paper.pdf | null | null | null | @InProceedings{Zhang_2021_ICCV,
author = {Zhang, Lin and Luo, Yong and Bai, Yan and Du, Bo and Duan, Ling-Yu},
title = {Federated Learning for Non-IID Data via Unified Feature Learning and Optimization Objective Alignment},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer... | Federated Learning (FL) aims to establish a shared model across decentralized clients under the privacy-preserving constraint. Despite certain success, it is still challenging for FL to deal with non-IID (non-independent and identical distribution) client data, which is a general scenario in real-world FL tasks. It has... |
Yang_Self-Supervised_Video_Object_Segmentation_by_Motion_Grouping_ICCV_2021_paper | Self-Supervised Video Object Segmentation by Motion Grouping | [
"Charig Yang",
"Hala Lamdouar",
"Erika Lu",
"Andrew Zisserman",
"Weidi Xie"
] | https://openaccess.thecvf.com/content/ICCV2021/html/Yang_Self-Supervised_Video_Object_Segmentation_by_Motion_Grouping_ICCV_2021_paper.html | https://openaccess.thecvf.com/content/ICCV2021/papers/Yang_Self-Supervised_Video_Object_Segmentation_by_Motion_Grouping_ICCV_2021_paper.pdf | https://openaccess.thecvf.com/content/ICCV2021/supplemental/Yang_Self-Supervised_Video_Object_ICCV_2021_supplemental.pdf | 2104.07658 | cvf | @InProceedings{Yang_2021_ICCV,
author = {Yang, Charig and Lamdouar, Hala and Lu, Erika and Zisserman, Andrew and Xie, Weidi},
title = {Self-Supervised Video Object Segmentation by Motion Grouping},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
month... | Animals have evolved highly functional visual systems to understand motion, assisting perception even under complex environments. In this paper, we work towards developing a computer vision system able to segment objects by exploiting motion cues, i.e. motion segmentation. To achieve this, we introduce a simple variant... |
Das_End-to-End_Piece-Wise_Unwarping_of_Document_Images_ICCV_2021_paper | End-to-End Piece-Wise Unwarping of Document Images | [
"Sagnik Das",
"Kunwar Yashraj Singh",
"Jon Wu",
"Erhan Bas",
"Vijay Mahadevan",
"Rahul Bhotika",
"Dimitris Samaras"
] | https://openaccess.thecvf.com/content/ICCV2021/html/Das_End-to-End_Piece-Wise_Unwarping_of_Document_Images_ICCV_2021_paper.html | https://openaccess.thecvf.com/content/ICCV2021/papers/Das_End-to-End_Piece-Wise_Unwarping_of_Document_Images_ICCV_2021_paper.pdf | null | null | null | @InProceedings{Das_2021_ICCV,
author = {Das, Sagnik and Singh, Kunwar Yashraj and Wu, Jon and Bas, Erhan and Mahadevan, Vijay and Bhotika, Rahul and Samaras, Dimitris},
title = {End-to-End Piece-Wise Unwarping of Document Images},
booktitle = {Proceedings of the IEEE/CVF International Conference on C... | Document unwarping attempts to undo the physical deformation of the paper and recover a 'flatbed' scanned document-image for downstream tasks such as OCR. Current state-of-the-art relies on global unwarping of the document which is not robust to local deformation changes. Moreover, a global unwarping often produces spu... |
Ronen_4D_Cloud_Scattering_Tomography_ICCV_2021_paper | 4D Cloud Scattering Tomography | [
"Roi Ronen",
"Yoav Y. Schechner",
"Eshkol Eytan"
] | https://openaccess.thecvf.com/content/ICCV2021/html/Ronen_4D_Cloud_Scattering_Tomography_ICCV_2021_paper.html | https://openaccess.thecvf.com/content/ICCV2021/papers/Ronen_4D_Cloud_Scattering_Tomography_ICCV_2021_paper.pdf | https://openaccess.thecvf.com/content/ICCV2021/supplemental/Ronen_4D_Cloud_Scattering_ICCV_2021_supplemental.pdf | null | null | @InProceedings{Ronen_2021_ICCV,
author = {Ronen, Roi and Schechner, Yoav Y. and Eytan, Eshkol},
title = {4D Cloud Scattering Tomography},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
month = {October},
year = {2021},
pages = {5... | We derive computed tomography (CT) of a time-varying volumetric scattering object, using a small number of moving cameras. We focus on passive tomography of dynamic clouds, as clouds have a major effect on the Earth's climate. State of the art scattering CT assumes a static object. Existing 4D CT methods rely on a line... |
Xu_Weakly_Supervised_Representation_Learning_With_Coarse_Labels_ICCV_2021_paper | Weakly Supervised Representation Learning With Coarse Labels | [
"Yuanhong Xu",
"Qi Qian",
"Hao Li",
"Rong Jin",
"Juhua Hu"
] | https://openaccess.thecvf.com/content/ICCV2021/html/Xu_Weakly_Supervised_Representation_Learning_With_Coarse_Labels_ICCV_2021_paper.html | https://openaccess.thecvf.com/content/ICCV2021/papers/Xu_Weakly_Supervised_Representation_Learning_With_Coarse_Labels_ICCV_2021_paper.pdf | https://openaccess.thecvf.com/content/ICCV2021/supplemental/Xu_Weakly_Supervised_Representation_ICCV_2021_supplemental.pdf | 2005.09681 | cvf | @InProceedings{Xu_2021_ICCV,
author = {Xu, Yuanhong and Qian, Qi and Li, Hao and Jin, Rong and Hu, Juhua},
title = {Weakly Supervised Representation Learning With Coarse Labels},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
month = {October},
... | With the development of computational power and techniques for data collection, deep learning demonstrates a superior performance over most existing algorithms on visual benchmark data sets. Many efforts have been devoted to studying the mechanism of deep learning. One important observation is that deep learning can le... |
Park_Asymmetric_Bilateral_Motion_Estimation_for_Video_Frame_Interpolation_ICCV_2021_paper | Asymmetric Bilateral Motion Estimation for Video Frame Interpolation | [
"Junheum Park",
"Chul Lee",
"Chang-Su Kim"
] | https://openaccess.thecvf.com/content/ICCV2021/html/Park_Asymmetric_Bilateral_Motion_Estimation_for_Video_Frame_Interpolation_ICCV_2021_paper.html | https://openaccess.thecvf.com/content/ICCV2021/papers/Park_Asymmetric_Bilateral_Motion_Estimation_for_Video_Frame_Interpolation_ICCV_2021_paper.pdf | https://openaccess.thecvf.com/content/ICCV2021/supplemental/Park_Asymmetric_Bilateral_Motion_ICCV_2021_supplemental.pdf | 2108.06815 | cvf | @InProceedings{Park_2021_ICCV,
author = {Park, Junheum and Lee, Chul and Kim, Chang-Su},
title = {Asymmetric Bilateral Motion Estimation for Video Frame Interpolation},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
month = {October},
year ... | We propose a novel video frame interpolation algorithm based on asymmetric bilateral motion estimation (ABME), which synthesizes an intermediate frame between two input frames. First, we predict symmetric bilateral motion fields to interpolate an anchor frame. Second, we estimate asymmetric bilateral motions fields fro... |
Shao_LocalTrans_A_Multiscale_Local_Transformer_Network_for_Cross-Resolution_Homography_Estimation_ICCV_2021_paper | LocalTrans: A Multiscale Local Transformer Network for Cross-Resolution Homography Estimation | [
"Ruizhi Shao",
"Gaochang Wu",
"Yuemei Zhou",
"Ying Fu",
"Lu Fang",
"Yebin Liu"
] | https://openaccess.thecvf.com/content/ICCV2021/html/Shao_LocalTrans_A_Multiscale_Local_Transformer_Network_for_Cross-Resolution_Homography_Estimation_ICCV_2021_paper.html | https://openaccess.thecvf.com/content/ICCV2021/papers/Shao_LocalTrans_A_Multiscale_Local_Transformer_Network_for_Cross-Resolution_Homography_Estimation_ICCV_2021_paper.pdf | https://openaccess.thecvf.com/content/ICCV2021/supplemental/Shao_LocalTrans_A_Multiscale_ICCV_2021_supplemental.pdf | 2106.04067 | cvf | @InProceedings{Shao_2021_ICCV,
author = {Shao, Ruizhi and Wu, Gaochang and Zhou, Yuemei and Fu, Ying and Fang, Lu and Liu, Yebin},
title = {LocalTrans: A Multiscale Local Transformer Network for Cross-Resolution Homography Estimation},
booktitle = {Proceedings of the IEEE/CVF International Conference... | Cross-resolution image alignment is a key problem in multiscale gigapixel photography, which requires to estimate homography matrix using images with large resolution gap. Existing deep homography methods concatenate the input images or features, neglecting the explicit formulation of correspondences between them, whic... |
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