Knowledge Commons of Institute of Automation，CAS
| Semantic-spatial fusion network for human parsing | |
Zhang, Xiaomei1,2; Chen, Yingying1,2 ; Zhu, Bingke1,2; Wang, Jinqiao1,2; Tang, Ming1,2
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| Source Publication | NEUROCOMPUTING
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| ISSN | 0925-2312 |
| 2020-08-18 | |
| Volume | 402Pages:375-383 |
| Corresponding Author | Chen, Yingying(yingying.chen@nlpr.ia.ac.cn) |
| Abstract | Recently, many methods have united low-level and high-level features to generate the desired accurate high-resolution prediction for human parsing. Nevertheless, there exists a semantic-spatial gap between low-level and high-level features in some methods, i.e., high-level features represent more semantics and less spatial details, while low-level ones have less semantics and more spatial details. In this paper, we propose a Semantic-Spatial Fusion Network (SSFNet) for human parsing to shrink the gap, which generates the accurate high-resolution prediction by aggregating multi-resolution features. SSFNet includes two models, a semantic modulation model and a resolution-aware model. The semantic modulation model guides spatial details with semantics and then effectively facilitates the feature fusion, narrowing the gap. The resolution-aware model sufficiently boosts the feature fusion and obtains multi-receptive-fields, which generates reliable and fine-grained high-resolution features for each branch, in bottom-up and top-down processes. Extensive experiments on three public datasets, PASCAL-Person-Part, LIP and PPSS, show that SSFNet achieves significant improvements over state-of-the-art methods. (C) 2020 Elsevier B.V. All rights reserved. |
| Keyword | SSFNet Semantic modulation model Resolution-aware model Human parsing |
| DOI | 10.1016/j.neucom.2020.03.096 |
| WOS Keyword | SEGMENTATION ; MODELS |
| Indexed By | SCI |
| Language | 英语 |
| Funding Project | National Natural Science Foundation of China[61976210] ; National Natural Science Foundation of China[61772527] |
| Funding Organization | National Natural Science Foundation of China |
| WOS Research Area | Computer Science |
| WOS Subject | Computer Science, Artificial Intelligence |
| WOS ID | WOS:000538815500005 |
| Publisher | ELSEVIER |
| Citation statistics | |
| Document Type | 期刊论文 |
| Identifier | http://ir.ia.ac.cn/handle/173211/39811 |
| Collection | 中国科学院自动化研究所 |
| Corresponding Author | Chen, Yingying |
| Affiliation | 1.Chinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, 95 Zhongguancun East Rd, Beijing 100190, Peoples R China 2.Univ Chinese Acad Sci, Sch Artificial Intelligence, Beijing 100049, Peoples R China |
| First Author Affilication | Chinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, Beijing 100190, Peoples R China |
| Corresponding Author Affilication | Chinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, Beijing 100190, Peoples R China |
| Recommended Citation GB/T 7714 | Zhang, Xiaomei,Chen, Yingying,Zhu, Bingke,et al. Semantic-spatial fusion network for human parsing[J]. NEUROCOMPUTING,2020,402:375-383. |
| APA | Zhang, Xiaomei,Chen, Yingying,Zhu, Bingke,Wang, Jinqiao,&Tang, Ming.(2020).Semantic-spatial fusion network for human parsing.NEUROCOMPUTING,402,375-383. |
| MLA | Zhang, Xiaomei,et al."Semantic-spatial fusion network for human parsing".NEUROCOMPUTING 402(2020):375-383. |
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