Progressively Refined Face Detection Through Semantics-Enriched Representation Learning | |
Li, Zhihang1,2,3![]() ![]() ![]() | |
发表期刊 | IEEE TRANSACTIONS ON INFORMATION FORENSICS AND SECURITY
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ISSN | 1556-6013 |
2020 | |
卷号 | 15期号:1页码:1394-1406 |
摘要 | Feature pyramids aim to learn multi-scale representations for detecting faces over various scales. However, they often lack adequate context over different scales, especially when there are many tiny faces in the wild. In this paper, we propose an attention-guided semantically enriched feature aggregation framework to learn a feature pyramid with rich semantics at all scales for face detection. Specifically, high-level abstract features are directly integrated into low-level representations by skip connections to retain as much semantic as possible. In addition, an attention mechanism is employed as a gate to emphasize relevant features and suppress useless features during feature fusion. Inspired by human visual perception of tiny faces, we specially design a deep progressive refined loss (DPRL) to effectively facilitate feature learning. According to the above principles, we design and investigate various feature pyramid frameworks through extensive experiments. Finally, two typical structures named Centralized Attention Feature (CAF) and Distributed Attention Feature (DAF) are proposed for face detection, which are in-place and end-to-end trainable. Extensive experiments across different aggregation architectures on four challenging face detection benchmarks demonstrate the superiority of our framework over state-of-the-art methods. |
关键词 | Face detection object detection |
DOI | 10.1109/TIFS.2019.2941800 |
收录类别 | SCI |
语种 | 英语 |
资助项目 | State Key Development Program[2016YFB1001001] ; National Natural Science Foundation of China[61622310] ; Beijing Natural Science Foundation[JQ18017] |
项目资助者 | State Key Development Program ; National Natural Science Foundation of China ; Beijing Natural Science Foundation |
WOS研究方向 | Computer Science ; Engineering |
WOS类目 | Computer Science, Theory & Methods ; Engineering, Electrical & Electronic |
WOS记录号 | WOS:000619201700003 |
出版者 | IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC |
七大方向——子方向分类 | 生物特征识别 |
引用统计 | |
文献类型 | 期刊论文 |
条目标识符 | http://ir.ia.ac.cn/handle/173211/43091 |
专题 | 模式识别实验室 |
通讯作者 | He, Ran |
作者单位 | 1.Chinese Acad Sci, Ctr Res Intelligent Percept & Comp, Natl Lab Pattern Recognit, Beijing 100190, Peoples R China 2.Chinese Acad Sci, Inst Automat, CAS Ctr Excellence Brain Sci & Intelligence Techn, Beijing 100190, Peoples R China 3.Univ Chinese Acad Sci, Sch Artificial Intelligence, Beijing 100049, Peoples R China 4.Baidu Inc, Beijing 100085, Peoples R China 5.Chinese Acad Sci, Inst Automat, Ctr Res Intelligent Percept & Comp, Natl Lab Pattern Recognit, Beijing 100190, Peoples R China |
第一作者单位 | 模式识别国家重点实验室; 中国科学院自动化研究所 |
通讯作者单位 | 模式识别国家重点实验室; 中国科学院自动化研究所 |
推荐引用方式 GB/T 7714 | Li, Zhihang,Tang, Xu,Wu, Xiang,et al. Progressively Refined Face Detection Through Semantics-Enriched Representation Learning[J]. IEEE TRANSACTIONS ON INFORMATION FORENSICS AND SECURITY,2020,15(1):1394-1406. |
APA | Li, Zhihang,Tang, Xu,Wu, Xiang,Liu, Jingtuo,&He, Ran.(2020).Progressively Refined Face Detection Through Semantics-Enriched Representation Learning.IEEE TRANSACTIONS ON INFORMATION FORENSICS AND SECURITY,15(1),1394-1406. |
MLA | Li, Zhihang,et al."Progressively Refined Face Detection Through Semantics-Enriched Representation Learning".IEEE TRANSACTIONS ON INFORMATION FORENSICS AND SECURITY 15.1(2020):1394-1406. |
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