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Hierarchical Attention for Part-Aware Face Detection

文献类型：期刊论文

作者Wu, Shuzhe2,3; Kan, Meina3; Shan, Shiguang1,2,3; Chen, Xilin1,3
刊名INTERNATIONAL JOURNAL OF COMPUTER VISION
出版日期2019-06-01
卷号127期号:6-7页码:560-578
关键词Hierarchical attention Face detection Object detection Deformation Part-aware
ISSN号0920-5691
DOI10.1007/s11263-019-01157-5
英文摘要Expressive representations for characterizing face appearances are essential for accurate face detection. Due to different poses, scales, illumination, occlusion, etc, face appearances generally exhibit substantial variations, and the contents of each local region (facial part) vary from one face to another. Current detectors, however, particularly those based on convolutional neural networks, apply identical operations (e.g. convolution or pooling) to all local regions on each face for feature aggregation (in a generic sliding-window configuration), and take all local features as equally effective for the detection task. In such methods, not only is each local feature suboptimal due to ignoring region-wise distinctions, but also the overall face representations are semantically inconsistent. To address the issue, we design a hierarchical attention mechanism to allow adaptive exploration of local features. Given a face proposal, part-specific attention modeled as learnable Gaussian kernels is proposed to search for proper positions and scales of local regions to extract consistent and informative features of facial parts. Then face-specific attention predicted with LSTM is introduced to model relations between the local parts and adjust their contributions to the detection tasks. Such hierarchical attention leads to a part-aware face detector, which forms more expressive and semantically consistent face representations. Extensive experiments are performed on three challenging face detection datasets to demonstrate the effectiveness of our hierarchical attention and make comparisons with state-of-the-art methods.
资助项目National Key R&D Program of China[2017YFA0700800] ; Natural Science Foundation of China[61390511] ; Natural Science Foundation of China[61650202] ; Natural Science Foundation of China[61772496] ; Natural Science Foundation of China[61402443]
WOS研究方向Computer Science
语种英语
WOS记录号WOS:000468525900003
出版者SPRINGER
源URL[http://119.78.100.204/handle/2XEOYT63/4229]  
专题中国科学院计算技术研究所期刊论文_英文
通讯作者Shan, Shiguang
作者单位1.Chinese Acad Sci, Ctr Excellence Brain Sci & Intelligence Technol, Shanghai 200031, Peoples R China
2.UCAS, Beijing 100049, Peoples R China
3.Chinese Acad Sci, ICT, Key Lab Intelligent Informat Proc, Beijing 100190, Peoples R China
推荐引用方式
GB/T 7714		 Wu, Shuzhe,Kan, Meina,Shan, Shiguang,et al. Hierarchical Attention for Part-Aware Face Detection[J]. INTERNATIONAL JOURNAL OF COMPUTER VISION,2019,127(6-7):560-578.
APA Wu, Shuzhe,Kan, Meina,Shan, Shiguang,&Chen, Xilin.(2019).Hierarchical Attention for Part-Aware Face Detection.INTERNATIONAL JOURNAL OF COMPUTER VISION,127(6-7),560-578.
MLA Wu, Shuzhe,et al."Hierarchical Attention for Part-Aware Face Detection".INTERNATIONAL JOURNAL OF COMPUTER VISION 127.6-7(2019):560-578.

入库方式： OAI收割

来源：计算技术研究所

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