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Learning Coexistence Discriminative Features for Multi-Class Object Detection

文献类型：期刊论文

作者Yao, Chao2,3; Sun, Pengfei2; Zhi, Ruicong3; Shen, Yanfei1,4
刊名IEEE ACCESS
出版日期2018
卷号6页码:37676-37684
ISSN号2169-3536
关键词Object detection faster R-CNN coexistence relation multi-class objects class attention map
DOI10.1109/ACCESS.2018.2852728
英文摘要Existing methods on object detection have the ability to learn the discriminative features of local regions for object recognition; however, the coexistence relation between the multi-class objects could also benefit recognition. In this paper, we propose to learn the coexistence discriminative features for multi-class object detection. Given an image with multiple class objects, the strong supervision of the region-based annotations are first used as the image-level label to learn the independent discriminative features for each class. Then, the coexistence relation is fused as coexistence feature based on the attention mechanism. By combining the independent discriminative features and coexistence feature, the classification performance of multi-class object proposals can be consistently improved. Experimental results prove that the proposed end-to-end network outperforms the state-of-the-art object detection approaches, and the learned discriminative features can effectively capture the coexistence relations to improve classification performance of multi-class objects in the object detection task.
资助项目National Natural Science Foundation of China[61471343] ; National Natural Science Foundation of China[61701036] ; Fundamental Research Funds for the Central Universities[2017RC52]
WOS研究方向Computer Science ; Engineering ; Telecommunications
语种英语
出版者IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
WOS记录号WOS:000439698700097
源URL[http://119.78.100.204/handle/2XEOYT63/4560]  
专题中国科学院计算技术研究所期刊论文_英文
通讯作者Zhi, Ruicong; Shen, Yanfei
作者单位1.Beijing Sport Univ, Sports & Engn Coll, Beijing 100084, Peoples R China
2.Beijing Univ Posts & Telecommun, Inst Sensing Technol & Business, Beijing 100876, Peoples R China
3.Univ Sci & Technol Beijing, Sch Comp & Commun Engn, Beijing 10083, Peoples R China
4.Chinese Acad Sci, Inst Comp Technol, Beijing 100190, Peoples R China
推荐引用方式
GB/T 7714		 Yao, Chao,Sun, Pengfei,Zhi, Ruicong,et al. Learning Coexistence Discriminative Features for Multi-Class Object Detection[J]. IEEE ACCESS,2018,6:37676-37684.
APA Yao, Chao,Sun, Pengfei,Zhi, Ruicong,&Shen, Yanfei.(2018).Learning Coexistence Discriminative Features for Multi-Class Object Detection.IEEE ACCESS,6,37676-37684.
MLA Yao, Chao,et al."Learning Coexistence Discriminative Features for Multi-Class Object Detection".IEEE ACCESS 6(2018):37676-37684.

入库方式： OAI收割

来源：计算技术研究所

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