Knowledge Commons of Institute of Automation,CAS
Semantic-spatial fusion network for human parsing | |
Zhang, Xiaomei1,2![]() ![]() ![]() ![]() ![]() | |
发表期刊 | NEUROCOMPUTING
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ISSN | 0925-2312 |
2020-08-18 | |
卷号 | 91期号:402页码:375-383 |
摘要 | 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. |
关键词 | SSFNet Semantic modulation model Resolution-aware model Human parsing |
DOI | 10.1016/j.neucom.2020.03.096 |
关键词[WOS] | SEGMENTATION ; MODELS |
收录类别 | SCI |
语种 | 英语 |
资助项目 | National Natural Science Foundation of China[61976210] ; National Natural Science Foundation of China[61772527] |
项目资助者 | National Natural Science Foundation of China |
WOS研究方向 | Computer Science |
WOS类目 | Computer Science, Artificial Intelligence |
WOS记录号 | WOS:000538815500005 |
出版者 | ELSEVIER |
七大方向——子方向分类 | 图像视频处理与分析 |
引用统计 | |
文献类型 | 期刊论文 |
条目标识符 | http://ir.ia.ac.cn/handle/173211/39811 |
专题 | 紫东太初大模型研究中心_图像与视频分析 |
通讯作者 | Chen, Yingying |
作者单位 | 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 |
第一作者单位 | 模式识别国家重点实验室 |
通讯作者单位 | 模式识别国家重点实验室 |
推荐引用方式 GB/T 7714 | Zhang, Xiaomei,Chen, Yingying,Zhu, Bingke,et al. Semantic-spatial fusion network for human parsing[J]. NEUROCOMPUTING,2020,91(402):375-383. |
APA | Zhang, Xiaomei,Chen, Yingying,Zhu, Bingke,Wang, Jinqiao,&Tang, Ming.(2020).Semantic-spatial fusion network for human parsing.NEUROCOMPUTING,91(402),375-383. |
MLA | Zhang, Xiaomei,et al."Semantic-spatial fusion network for human parsing".NEUROCOMPUTING 91.402(2020):375-383. |
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