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检索条件"任意字段=Conference on Computer Vision and Pattern Recognition"
30983 条 记 录,以下是4691-4700 订阅
排序:
Exploring and Distilling Posterior and Prior Knowledge for Radiology Report Generation
Exploring and Distilling Posterior and Prior Knowledge for R...
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IEEE/CVF conference on computer vision and pattern recognition (CVPR)
作者: Liu, Fenglin Wu, Xian Ge, Shen Fan, Wei Zou, Yuexian Peking Univ Sch ECE ADSPLAB Beijing Peoples R China Tencent Med AI Lab Beijing Peoples R China Peng Cheng Lab Shenzhen Peoples R China
Automatically generating radiology reports can improve current clinical practice in diagnostic radiology. On one hand, it can relieve radiologists from the heavy burden of report writing;On the other hand, it can remi... 详细信息
来源: 评论
Learning Semantic Associations for Mirror Detection
Learning Semantic Associations for Mirror Detection
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IEEE/CVF conference on computer vision and pattern recognition (CVPR)
作者: Guan, Huankang Lin, Jiaying Lau, Rynson W. H. City Univ Hong Kong Hong Kong Peoples R China
Mirrors generally lack a consistent visual appearance, making mirror detection very challenging. Although recent works that are based on exploiting contextual contrasts and corresponding relations have achieved good r... 详细信息
来源: 评论
Dynamic Class Queue for Large Scale Face recognition In the Wild
Dynamic Class Queue for Large Scale Face Recognition In the ...
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IEEE/CVF conference on computer vision and pattern recognition (CVPR)
作者: Li, Bi Xi, Teng Zhang, Gang Feng, Haocheng Han, Junyu Liu, Jingtuo Ding, Errui Liu, Wenyu Baidu Inc Dept Comp Vis Technol VIS Beijing Peoples R China Huazhong Univ Sci & Technol Sch Elect Informat & Commun Wuhan Peoples R China Tsinghua Univ Dept Comp Sci & Technol Beijing Peoples R China
Learning discriminative representation using large-scale face datasets in the wild is crucial for real-world applications, yet it remains challenging. The difficulties lie in many aspects and this work focus on comput... 详细信息
来源: 评论
Lepard: Learning partial point cloud matching in rigid and deformable scenes
Lepard: Learning partial point cloud matching in rigid and d...
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IEEE/CVF conference on computer vision and pattern recognition (CVPR)
作者: Li, Yang Harada, Tatsuya Univ Tokyo Tokyo Japan RIKEN Wako Saitama Japan
We present Lepard, a Learning based approach for partial point cloud matching in rigid and deformable scenes. The key characteristics are the following techniques that exploit 3D positional knowledge for point cloud m... 详细信息
来源: 评论
Few-shot Keypoint Detection with Uncertainty Learning for Unseen Species
Few-shot Keypoint Detection with Uncertainty Learning for Un...
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IEEE/CVF conference on computer vision and pattern recognition (CVPR)
作者: Lu, Changsheng Koniusz, Piotr Australian Natl Univ Canberra ACT Australia CSIRO Data61 Canberra ACT Australia
Current non-rigid object keypoint detectors perform well on a chosen kind of species and body parts, and require a large amount of labelled keypoints for training. Moreover, their heatmaps, tailored to specific body p... 详细信息
来源: 评论
Collaborative and Adversarial Network for Unsupervised domain adaptation  31
Collaborative and Adversarial Network for Unsupervised domai...
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31st IEEE/CVF conference on computer vision and pattern recognition (CVPR)
作者: Zhang, Weichen Ouyang, Wanli Li, Wen Xu, Dong Univ Sydney Sch Elect & Informat Engn Sydney NSW Australia Swiss Fed Inst Technol Comp Vis Lab Zurich Switzerland
In this paper, we propose a new unsupervised domain adaptation approach called Collaborative and Adversarial Network (CAN) through domain-collaborative and domain adversarial training of neural networks. We add severa... 详细信息
来源: 评论
A Closer Look at the Few-Shot Adaptation of Large vision-Language Models
A Closer Look at the Few-Shot Adaptation of Large Vision-Lan...
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IEEE/CVF conference on computer vision and pattern recognition (CVPR)
作者: Iguez, Julio Silva-Rodr Hajimiri, Sina Ben Ayed, Ismail Dolz, Jose ETS Montreal Montreal PQ Canada
Efficient transfer learning (ETL) is receiving increasing attention to adapt large pre-trained language-vision models on downstream tasks with a few labeled samples. While significant progress has been made, we reveal...
来源: 评论
DeepMetaHandles: Learning Deformation Meta-Handles of 3D Meshes with Biharmonic Coordinates
DeepMetaHandles: Learning Deformation Meta-Handles of 3D Mes...
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IEEE/CVF conference on computer vision and pattern recognition (CVPR)
作者: Liu, Minghua Sung, Minhyuk Mech, Radomir Su, Hao Univ Calif San Diego San Diego CA 92103 USA Korea Adv Inst Sci & Technol Daejeon South Korea Adobe Res San Jose CA USA
We propose DeepMetaHandles, a 3D conditional generative model based on mesh deformation. Given a collection of 3D meshes of a category and their deformation handles (control points), our method learns a set of metahan... 详细信息
来源: 评论
Structured Multi-Level Interaction Network for Video Moment Localization via Language Query
Structured Multi-Level Interaction Network for Video Moment ...
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IEEE/CVF conference on computer vision and pattern recognition (CVPR)
作者: Wang, Hao Zha, Zheng-Jun Li, Liang Liu, Dong Luo, Jiebo Univ Sci & Technol China Hefei Peoples R China Chinese Acad Sci Inst Comp Technol Beijing Peoples R China Univ Rochester Rochester NY 14627 USA
We address the problem of localizing a specific moment described by a natural language query. Existing works interact the query with either video frame or moment proposal, and neglect the inherent structure of moment ... 详细信息
来源: 评论
DualGraph: A graph-based method for reasoning about label noise
DualGraph: A graph-based method for reasoning about label no...
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IEEE/CVF conference on computer vision and pattern recognition (CVPR)
作者: Zhang, HaiYang Xing, XiMing Liu, Liang Beijing Univ Posts & Telecommun Sch Comp Sci Beijing Peoples R China
Unreliable labels derived from large-scale dataset prevent neural networks from fully exploring the data. Existing methods of learning with noisy labels primarily take noise-cleaning-based and sample-selection-based m... 详细信息
来源: 评论