Location Sensitive Network for Human Instance Segmentation

doi:10.1109/TIP.2021.3107210

	Location Sensitive Network for Human Instance Segmentation
	Zhang, Xiangzhou 1; Ma, Bingpeng 2; Chang, Hong 2,3; Shan, Shiguang 3,4; Chen, Xilin 2,3
	2021
发表期刊	IEEE TRANSACTIONS ON IMAGE PROCESSING
ISSN	1057-7149
卷号	30 页码:7649-7662
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.
关键词	Image segmentation Prototypes Heating systems Task analysis Semantics Feature extraction Detectors Human instance segmentation spatial invariance coordinates encoding points representation
DOI	10.1109/TIP.2021.3107210
收录类别	SCI
语种	英语
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
WOS类目	Computer Science, Artificial Intelligence ; Engineering, Electrical & Electronic
WOS记录号	WOS:000693758500010
出版者	IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
引用统计	被引频次：4[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	http://119.78.100.204/handle/2XEOYT63/17149
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.