标题:Channel-Wise Feature Response Based Deep Saliency Detection
作者:Li, Cuiping ;Chen, Zhenxue ;Liu, Chengyun
通讯作者:Liu, Chengyun
作者机构:[Li, Cuiping ;Chen, Zhenxue ;Liu, Chengyun ] School of Control Science and Engineering, Shandong University, Jinan, China
会议名称:3rd IEEE International Conference on Advanced Robotics and Mechatronics, ICARM 2018
会议日期:July 18, 2018 - July 20, 2018
来源:ICARM 2018 - 2018 3rd International Conference on Advanced Robotics and Mechatronics
出版年:2018
页码:537-541
DOI:10.1109/ICARM.2018.8610858
摘要:Saliency detection based on deep learning has achieved excellent performance. This paper focuses on channel-wise feature response. Our model owns three structures: channel-wise extraction (CCFE), channel-wise hierarchical refinement (CHFR), and feature maps fusion (HFMF). The squeeze and excitation residual net is the base network used in this work, which can explicitly explore the dependencies among channels. CCFE is utilized to obtain global feature maps, which has a lot of information loss. Then, the CHFR structure is carried out to take advantage of spatial cues. At the end, HFMF is implemented to fuse the feature maps. The experimental results prove that the proposed algorithm has high efficiency and superior performance compared with other fourteen methods.
© 2018 IEEE.
收录类别:EI
资源类型:会议论文
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