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- FENG Xun
FENG Xun
Pattern Computing and Application Laboratory, School of Computer Science and Engineering, Nanjing University of Science & Technology, Nanjing 210094, China;Key Laboratory of Intelligent Perception and Systems for High-dimensional Information of Ministry of Education (Nanjing University of Science & Technology), Nanjing 210094, China;Key Laboratory of Image and Video Understanding for Social Security of Jiangsu Province (Nanjing University of Science & Technology), Nanjing 210094, China
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Search for this author on this site - YANG Jian
YANG Jian
Pattern Computing and Application Laboratory, School of Computer Science and Engineering, Nanjing University of Science & Technology, Nanjing 210094, China;Key Laboratory of Intelligent Perception and Systems for High-dimensional Information of Ministry of Education (Nanjing University of Science & Technology), Nanjing 210094, China;Key Laboratory of Image and Video Understanding for Social Security of Jiangsu Province (Nanjing University of Science & Technology), Nanjing 210094, China
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Search for this author on this site - ZHOU Tao
ZHOU Tao
Pattern Computing and Application Laboratory, School of Computer Science and Engineering, Nanjing University of Science & Technology, Nanjing 210094, China;Key Laboratory of Intelligent Perception and Systems for High-dimensional Information of Ministry of Education (Nanjing University of Science & Technology), Nanjing 210094, China;Key Laboratory of Image and Video Understanding for Social Security of Jiangsu Province (Nanjing University of Science & Technology), Nanjing 210094, China
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Search for this author on this site - GONG Chen
GONG Chen
Pattern Computing and Application Laboratory, School of Computer Science and Engineering, Nanjing University of Science & Technology, Nanjing 210094, China;Key Laboratory of Intelligent Perception and Systems for High-dimensional Information of Ministry of Education (Nanjing University of Science & Technology), Nanjing 210094, China;Key Laboratory of Image and Video Understanding for Social Security of Jiangsu Province (Nanjing University of Science & Technology), Nanjing 210094, China
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Weakly supervised object localization aims to train target locators only by image-level labels instead of accurate location annotations for algorithm training. Some existing methods can only identify the most discriminative region of the target object and are incapable of covering the complete object, or can easily be misled by irrelevant background information, thereby leading to inaccurate object locations. Therefore, this study proposes a weakly supervised object localization algorithm based on attention mechanism and categorical hierarchy. The proposed method extracts a more complete object area by performing mean segmentation on the attention map of the convolutional neural network. In addition, the category hierarchy network is utilized to weaken the attention caused by background areas, which achieves more accurate object location results. Extensive experimental results on multiple public datasets show that the proposed method can yield better localization effects than other weakly supervised object localization methods under various evaluation metrics.
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冯迅,杨健,周涛,宫辰.基于注意力机制及类别层次结构的弱监督目标定位.软件学报,2023,34(10):4916-4929
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History
- Received:March 22,2021
- Revised:October 25,2021
- Online: April 04,2023
- Published: October 06,2023
Copyright: Institute of Software, Chinese Academy of Sciences Beijing ICP No. 05046678-4
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