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检索条件"任意字段=IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops"
12859 条 记 录,以下是4801-4810 订阅
排序:
DANNet: A One-Stage Domain Adaptation Network for Unsupervised Nighttime Semantic Segmentation
DANNet: A One-Stage Domain Adaptation Network for Unsupervis...
收藏 引用
ieee/cvf conference on computer vision and pattern recognition (CVPR)
作者: Wu, Xinyi Wu, Zhenyao Guo, Hao Ju, Lili Wang, Song Univ South Carolina Columbia SC 29208 USA Farsee2 Technol Ltd Shenzhen Guangdong Peoples R China
Semantic segmentation of nighttime images plays an equally important role as that of daytime images in autonomous driving, but the former is much more challenging due to poor illuminations and arduous human annotation... 详细信息
来源: 评论
Affordance Transfer Learning for Human-Object Interaction Detection
Affordance Transfer Learning for Human-Object Interaction De...
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ieee/cvf conference on computer vision and pattern recognition (CVPR)
作者: Hou, Zhi Yu, Baosheng Qiao, Yu Peng, Xiaojiang Tao, Dacheng Univ Sydney Fac Engn Sch Comp Sci Sydney NSW Australia Chinese Acad Sci Shenzhen Inst Adv Technol Beijing Peoples R China Shanghai AI Lab Shanghai Peoples R China Shenzhen Technol Univ Shenzhen Peoples R China
Reasoning the human-object interactions (HOI) is essential for deeper scene understanding, while object affordances (or functionalities) are of great importance for human to discover unseen HOIs with novel objects. In... 详细信息
来源: 评论
Group Collaborative Learning for Co-Salient Object Detection
Group Collaborative Learning for Co-Salient Object Detection
收藏 引用
ieee/cvf conference on computer vision and pattern recognition (CVPR)
作者: Fan, Qi Fan, Deng-Ping Fu, Huazhu Tang, Chi-Keung Shao, Ling Tai, Yu-Wing HKUST Hong Kong Peoples R China Incept Inst Artificial Intelligence Abu Dhabi U Arab Emirates Kuaishou Technol Beijing Peoples R China
We present a novel group collaborative learning framework (GCoNet) capable of detecting co-salient objects in real time (16ms), by simultaneously mining consensus representations at group level based on the two necess... 详细信息
来源: 评论
One Thing One Click: A Self-Training Approach for Weakly Supervised 3D Semantic Segmentation
One Thing One Click: A Self-Training Approach for Weakly Sup...
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ieee/cvf conference on computer vision and pattern recognition (CVPR)
作者: Liu, Zhengzhe Qi, Xiaojuan Fu, Chi-Wing Chinese Univ Hong Kong Hong Kong Peoples R China Univ Hong Kong Hong Kong Peoples R China
Point cloud semantic segmentation often requires large-scale annotated training data, but clearly, point-wise labels are too tedious to prepare. While some recent methods propose to train a 3D network with small perce... 详细信息
来源: 评论
Meta-Mining Discriminative Samples for Kinship Verification
Meta-Mining Discriminative Samples for Kinship Verification
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ieee/cvf conference on computer vision and pattern recognition (CVPR)
作者: Li, Wanhua Wang, Shiwei Lu, Jiwen Feng, Jianjiang Zhou, Jie Tsinghua Univ Dept Automat Beijing Peoples R China Beijing Natl Res Ctr Informat Sci & Technol Beijing Peoples R China Beijing Univ Posts & Telecommun Sch Modern Post Beijing Peoples R China
Kinship verification aims to find out whether there is a kin relation for a given pair of facial images. Kinship verification databases are born with unbalanced data. For a database with N positive kinship pairs, we n... 详细信息
来源: 评论
CoLA: Weakly-Supervised Temporal Action Localization with Snippet Contrastive Learning
CoLA: Weakly-Supervised Temporal Action Localization with Sn...
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ieee/cvf conference on computer vision and pattern recognition (CVPR)
作者: Zhang, Can Cao, Meng Yang, Dongming Chen, Jie Zou, Yuexian Peking Univ Sch Elect & Comp Engn Beijing Peoples R China Peng Cheng Lab Shenzhen Peoples R China
Weakly-supervised temporal action localization (WS-TAL) aims to localize actions in untrimmed videos with only video-level labels. Most existing models follow the "localization by classification" procedure: ... 详细信息
来源: 评论
Learning Spatially-Variant MAP Models for Non-blind Image Deblurring
Learning Spatially-Variant MAP Models for Non-blind Image De...
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ieee/cvf conference on computer vision and pattern recognition (CVPR)
作者: Dong, Jiangxin Roth, Stefan Schiele, Bernt Saarland Informat Campus MPI Informat Saarbrucken Germany Tech Univ Darmstadt Darmstadt Germany Hessian AI Darmstadt Germany
The classical maximum a-posteriori (MAP) framework for non-blind image deblurring requires defining suitable data and regularization terms, whose interplay yields the desired clear image through optimization. The vast... 详细信息
来源: 评论
Semantic Image Matting
Semantic Image Matting
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ieee/cvf conference on computer vision and pattern recognition (CVPR)
作者: Sun, Yanan Tang, Chi-Keung Tai, Yu-Wing HKUST Hong Kong Peoples R China Kuaishou Technol Beijing Peoples R China
Natural image matting separates the foreground from background in fractional occupancy which can be caused by highly transparent objects, complex foreground (e.g., net or tree), and/or objects containing very fine det... 详细信息
来源: 评论
Detecting Human-Object Interaction via Fabricated Compositional Learning
Detecting Human-Object Interaction via Fabricated Compositio...
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ieee/cvf conference on computer vision and pattern recognition (CVPR)
作者: Hou, Zhi Yu, Baosheng Qiao, Yu Peng, Xiaojiang Tao, Dacheng Univ Sydney Fac Engn Sch Comp Sci Sydney NSW Australia Chinese Acad Sci Shenzhen Inst Adv Technol Shenzhen Peoples R China Shanghai AI Lab Shanghai Peoples R China Shenzhen Technol Univ Shenzhen Peoples R China
Human-Object Interaction (HOI) detection, inferring the relationships between human and objects from images/videos, is a fundamental task for high-level scene understanding. However, HOI detection usually suffers from... 详细信息
来源: 评论
Scale-aware Automatic Augmentation for Object Detection
Scale-aware Automatic Augmentation for Object Detection
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ieee/cvf conference on computer vision and pattern recognition (CVPR)
作者: Chen, Yukang Li, Yanwei Kong, Tao Qi, Lu Chu, Ruihang Li, Lei Jia, Jiaya Chinese Univ Hong Kong Hong Kong Peoples R China ByteDance AI Lab Beijing Peoples R China SmartMore Hong Kong Peoples R China
We propose Scale-aware AutoAug to learn data augmentation policies for object detection. We define a new scaleaware search space, where both image- and box-level augmentations are designed for maintaining scale invari... 详细信息
来源: 评论