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Location Sensitive Network for Human Instance Segmentation

文献类型：期刊论文

作者Zhang, Xiangzhou1; Ma, Bingpeng2; Chang, Hong2,3; Shan, Shiguang3,4; Chen, Xilin2,3
刊名IEEE TRANSACTIONS ON IMAGE PROCESSING
出版日期2021
卷号30页码:7649-7662
关键词Image segmentation Prototypes Heating systems Task analysis Semantics Feature extraction Detectors Human instance segmentation spatial invariance coordinates encoding points representation
ISSN号1057-7149
DOI10.1109/TIP.2021.3107210
英文摘要Location is an important distinguishing information for instance segmentation. In this paper, we propose a novel model, called Location Sensitive Network (LSNet), for human instance segmentation. LSNet integrates instance-specific location information into one-stage segmentation framework. Specifically, in the segmentation branch, Pose Attention Module (PAM) encodes the location information into the attention regions through coordinates encoding. Based on the location information provided by PAM, the segmentation branch is able to effectively distinguish instances in feature-level. Moreover, we propose a combination operation named Keypoints Sensitive Combination (KSCom) to utilize the location information from multiple sampling points. These sampling points construct the points representation for instances via human keypoints and random points. Human keypoints provide the spatial locations and semantic information of the instances, and random points expand the receptive fields. Based on the points representation for each instance, KSCom effectively reduces the mis-classified pixels. Our method is validated by the experiments on public datasets. LSNet-5 achieves 56.2 mAP at 18.5 FPS on COCOPersons. Besides, the proposed method is significantly superior to its peers in the case of severe occlusion.
资助项目National Key Research and Development Program of China[2017YFA0700800] ; Natural Science Foundation of China (NSFC)[61876171] ; Natural Science Foundation of China (NSFC)[61976203]
WOS研究方向Computer Science ; Engineering
语种英语
WOS记录号WOS:000693758500010
出版者IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
源URL[http://119.78.100.204/handle/2XEOYT63/17148]  
专题中国科学院计算技术研究所期刊论文_英文
通讯作者Ma, Bingpeng
作者单位1.ShanghaiTech Univ, Sch Informat Sci & Technol, Shanghai 201210, Peoples R China
2.Univ Chinese Acad Sci, Sch Comp Sci & Technol, Beijing 100049, Peoples R China
3.Chinese Acad Sci, Inst Comp Technol, Chinese Acad Sci CAS, Key Lab Intelligent Informat Proc, Beijing 100190, Peoples R China
4.Chinese Acad Sci, Ctr Excellence Brain Sci & Intelligence Technol, Shanghai 200031, Peoples R China
推荐引用方式
GB/T 7714		 Zhang, Xiangzhou,Ma, Bingpeng,Chang, Hong,et al. Location Sensitive Network for Human Instance Segmentation[J]. IEEE TRANSACTIONS ON IMAGE PROCESSING,2021,30:7649-7662.
APA Zhang, Xiangzhou,Ma, Bingpeng,Chang, Hong,Shan, Shiguang,&Chen, Xilin.(2021).Location Sensitive Network for Human Instance Segmentation.IEEE TRANSACTIONS ON IMAGE PROCESSING,30,7649-7662.
MLA Zhang, Xiangzhou,et al."Location Sensitive Network for Human Instance Segmentation".IEEE TRANSACTIONS ON IMAGE PROCESSING 30(2021):7649-7662.

入库方式： OAI收割

来源：计算技术研究所

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