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检索条件"任意字段=Conference on Computer Vision and Pattern Recognition"
31017 条 记 录,以下是4861-4870 订阅
排序:
Neighborhood Contrastive Learning for Novel Class Discovery
Neighborhood Contrastive Learning for Novel Class Discovery
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IEEE/CVF conference on computer vision and pattern recognition (CVPR)
作者: Zhong, Zhun Fini, Enrico Roy, Subhankar Luo, Zhiming Ricci, Elisa Sebe, Nicu Univ Trento Trento Italy Xiamen Univ Xiamen Peoples R China Fdn Bruno Kessler Povo Italy
In this paper, we address Novel Class Discovery (NCD), the task of unveiling new classes in a set of unlabeled samples given a labeled dataset with known classes. We exploit the peculiarities of NCD to build a new fra... 详细信息
来源: 评论
Attendance Automation System with Facial Authorization and Body Temperature Using Cloud Based Viola-Jones Face recognition Algorithm  22nd
Attendance Automation System with Facial Authorization and B...
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22nd International conference on Hybrid Intelligent Systems, HIS 2022 and the 18th International conference on Information Assurance and Security, IAS 2022
作者: Devi Priya, R. Kirupa, P. Manoj Kumar, S. Mouthami, K. Department of Computer Science and Engineering Centre for IoT and Artificial Intelligence KPR Institute of Engineering and Technology Coimbatore India Department of Computer Science and Engineering KPR Institute of Engineering and Technology Coimbatore India
Face recognition works all over the world from various perspectives. In the field of attendance systems, many methodologies have faced various drawbacks. In the headset of attendance monitoring, the attendance is regi... 详细信息
来源: 评论
Pixel-wise Anomaly Detection in Complex Driving Scenes
Pixel-wise Anomaly Detection in Complex Driving Scenes
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IEEE/CVF conference on computer vision and pattern recognition (CVPR)
作者: Di Biase, Giancarlo Blum, Hermann Siegwart, Roland Cadena, Cesar Swiss Fed Inst Technol Zurich Switzerland
The inability of state-of-the-art semantic segmentation methods to detect anomaly instances hinders them from being deployed in safety-critical and complex applications, such as autonomous driving. Recent approaches h... 详细信息
来源: 评论
Learning by Watching
Learning by Watching
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IEEE/CVF conference on computer vision and pattern recognition (CVPR)
作者: Zhang, Jimuyang Ohn-Bar, Eshed Boston Univ Boston MA 02215 USA
When in a new situation or geographical location, human drivers have an extraordinary ability to watch others and learn maneuvers that they themselves may have never performed. In contrast, existing techniques for lea... 详细信息
来源: 评论
Counterfactual Zero-Shot and Open-Set Visual recognition
Counterfactual Zero-Shot and Open-Set Visual Recognition
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IEEE/CVF conference on computer vision and pattern recognition (CVPR)
作者: Yue, Zhongqi Wang, Tan Sun, Qianru Hua, Xian-Sheng Zhang, Hanwang Nanyang Technol Univ Singapore Singapore Singapore Management Univ Singapore Singapore Alibaba Grp Damo Acad Hangzhou Peoples R China
We present a novel counterfactual framework for both Zero-Shot Learning (ZSL) and Open-Set recognition (OSR), whose common challenge is generalizing to the unseen-classes by only training on the seen-classes. Our idea... 详细信息
来源: 评论
Area Calculation of Binary Images Based on Overlapping Rectangular Subpatterns  19
Area Calculation of Binary Images Based on Overlapping Recta...
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19th International conference on Natural Computation, Fuzzy Systems and Knowledge Discovery, ICNC-FSKD 2023
作者: Zheng, Yunping Peng, Haiping South China University of Technology School of Computer Science and Engineering Guangzhou China
Image representation is an important research topic in the field of computer vision, and pattern recognition. Also, applications based on image representation have an important role. Many image representation methods ... 详细信息
来源: 评论
Globally Optimal Relative Pose Estimation with Gravity Prior
Globally Optimal Relative Pose Estimation with Gravity Prior
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IEEE/CVF conference on computer vision and pattern recognition (CVPR)
作者: Ding, Yaqing Barath, Daniel Yang, Jian Kong, Hui Kukelova, Zuzana Nanjing Univ Sci & Technol Sch Comp Sci & Engn Nanjing Peoples R China Czech Tech Univ Fac Elect Engn Visual Recognit Grp Prague Czech Republic SZTAKI MPLab Budapest Hungary Swiss Fed Inst Technol Dept Comp Sci Zurich Switzerland
Smartphones, tablets and camera systems used, e.g., in cars and UAVs, are typically equipped with IMUs (inertial measurement units) that can measure the gravity vector accurately. Using this additional information, th... 详细信息
来源: 评论
Multiresolution Knowledge Distillation for Anomaly Detection
Multiresolution Knowledge Distillation for Anomaly Detection
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IEEE/CVF conference on computer vision and pattern recognition (CVPR)
作者: Salehi, Mohammadreza Sadjadi, Niousha Baselizadeh, Soroosh Rohban, Mohammad H. Rabiee, Hamid R. Sharif Univ Technol Dept Comp Engn Tehran Iran
Unsupervised representation learning has proved to be a critical component of anomaly detection/localization in images. The challenges to learn such a representation are two-fold. Firstly, the sample size is not often... 详细信息
来源: 评论
DCT-Mask: Discrete Cosine Transform Mask Representation for Instance Segmentation
DCT-Mask: Discrete Cosine Transform Mask Representation for ...
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IEEE/CVF conference on computer vision and pattern recognition (CVPR)
作者: Shen, Xing Yang, Jirui Wei, Chunbo Deng, Bing Huang, Jianqiang Hua, Xiansheng Cheng, Xiaoliang Liang, Kewei Zhejiang Univ Sch Math Sci Hangzhou Peoples R China Alibaba Grp DAMO Acad Hangzhou Peoples R China
Binary grid mask representation is broadly used in instance segmentation. A representative instantiation is Mask R-CNN which predicts masks on a 28 x 28 binary grid. Generally, a low-resolution grid is not sufficient ... 详细信息
来源: 评论
Tree-like Decision Distillation
Tree-like Decision Distillation
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IEEE/CVF conference on computer vision and pattern recognition (CVPR)
作者: Song, Jie Zhang, Haofei Wang, Xinchao Xue, Mengqi Chen, Ying Sun, Li Tao, Dacheng Song, Mingli Zhejiang Univ Hangzhou Peoples R China Natl Univ Singapore Singapore Singapore Univ Sydney Sydney NSW Australia Stevens Inst Technol Hoboken NJ 07030 USA
Knowledge distillation pursues a diminutive yet well-behaved student network by harnessing the knowledge learned by a cumbersome teacher model. Prior methods achieve this by making the student imitate shallow behavior... 详细信息
来源: 评论