Human Parsing With Part-Aware Relation Modeling
文献类型：期刊论文
| 作者 | Zhang, Xiaomei3,4 ; Chen, Yingying1; Tang, Ming1; Wang, Jinqiao1; Zhu, Xiangyu3,4; Lei, Zhen2,3
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| 刊名 | IEEE TRANSACTIONS ON MULTIMEDIA
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| 出版日期 | 2023 |
| 卷号 | 25页码:2601-2612 |
| ISSN号 | 1520-9210 |
| 关键词 | Human parsing modeling part-aware relation |
| DOI | 10.1109/TMM.2022.3148595 |
| 通讯作者 | Lei, Zhen(zlei@nlpr.ia.ac.cn) |
| 英文摘要 | In this paper, a Part-aware Relation Modeling (PRM) is developed to handle the task of human parsing. For pixel-level recognition, it is essential to generate features with adaptive context for various sizes and shapes of human parts. To address the issue, we adaptively capture contexts based on the part-aware relation mechanism. PRM mainly consists of three modules, including a part class module, a part-relation aggregation module, and a part-relation dispersion module. The part class module selectively enhances spatial details of the high-level features to obtain enhanced original features, and then extracts the high-level representations of every human part from a categorical perspective. The part-relation aggregation module is developed to extract the representative global context by exploring associated semantics of human parts, adaptively augmenting the context for human parts. The part-relation dispersion module is designed to generate the discriminative and effective local context and neglect the distracting one by making the affinity of human parts disperse. It ensures that features of the same class will be close to each other and away from those of different classes. By fusing the outputs of the two part-relation modules and the first outputs of the part class module, our PRM produces adaptive contextual features for diverse sizes of human parts, boosting the parsing accuracy. Extensive experiments are conducted to validate the effectiveness of our network, and a new state-of-the-art segmentation performance is achieved on three challenging human parsing datasets, i.e., PASCAL-Person-Part, LIP, and CIHP. PRM is also extended to other tasks like animal parsing, and exhibits its generality. |
| 资助项目 | National Key Research and Development Program of China[2020YFC2003901] ; National Natural Science Foundation of China[61976210] ; National Natural Science Foundation of China[61772527] ; National Natural Science Foundation of China[61806200] ; National Natural Science Foundation of China[61702510] ; National Natural Science Foundation of China[61876086] ; Youth Innovation Promotion Association CAS[Y2021131] |
| WOS研究方向 | Computer Science ; Telecommunications |
| 语种 | 英语 |
| 出版者 | IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC |
| WOS记录号 | WOS:001030640600011 |
| 资助机构 | National Key Research and Development Program of China ; National Natural Science Foundation of China ; Youth Innovation Promotion Association CAS |
| 源URL | [http://ir.ia.ac.cn/handle/173211/53780]  |
| 专题 | 多模态人工智能系统全国重点实验室 |
| 通讯作者 | Lei, Zhen |
| 作者单位 | 1.Chinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, Beijing, Peoples R China 2.Chinese Acad Sci, Hong Kong Inst Sci & Innovat, Ctr Artificial Intelligence & Robot, Hong Kong, Peoples R China 3.Chinese Acad Sci CASIA, Inst Automat, Ctr Biometr & Secur Res CBSR, Natl Lab Pattern Recognit NLPR, Beijing 100190, Peoples R China 4.Univ Chinese Acad Sci UCAS, Sch Artificial Intelligence, Beijing 100049, Peoples R China |
| 推荐引用方式 GB/T 7714 | Zhang, Xiaomei,Chen, Yingying,Tang, Ming,et al. Human Parsing With Part-Aware Relation Modeling[J]. IEEE TRANSACTIONS ON MULTIMEDIA,2023,25:2601-2612. |
| APA | Zhang, Xiaomei,Chen, Yingying,Tang, Ming,Wang, Jinqiao,Zhu, Xiangyu,&Lei, Zhen.(2023).Human Parsing With Part-Aware Relation Modeling.IEEE TRANSACTIONS ON MULTIMEDIA,25,2601-2612. |
| MLA | Zhang, Xiaomei,et al."Human Parsing With Part-Aware Relation Modeling".IEEE TRANSACTIONS ON MULTIMEDIA 25(2023):2601-2612. |
入库方式： OAI收割
来源：自动化研究所
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