Global-and-Local Collaborative Learning for Co-Salient Object Detection

doi:10.1109/TCYB.2022.3169431

	Global-and-Local Collaborative Learning for Co-Salient Object Detection
	Cong, Runmin 1,2,3; Yang, Ning 1,2; Li, Chongyi 4; Fu, Huazhu 5; Zhao, Yao 1,2; Huang, Qingming 6,7,8,9; Kwong, Sam 10,11
	2022-07-22
发表期刊	IEEE TRANSACTIONS ON CYBERNETICS
ISSN	2168-2267
页码	12
摘要	The goal of co-salient object detection (CoSOD) is to discover salient objects that commonly appear in a query group containing two or more relevant images. Therefore, how to effectively extract interimage correspondence is crucial for the CoSOD task. In this article, we propose a global-and-local collaborative learning (GLNet) architecture, which includes a global correspondence modeling (GCM) and a local correspondence modeling (LCM) to capture the comprehensive interimage corresponding relationship among different images from the global and local perspectives. First, we treat different images as different time slices and use 3-D convolution to integrate all intrafeatures intuitively, which can more fully extract the global group semantics. Second, we design a pairwise correlation transformation (PCT) to explore similarity correspondence between pairwise images and combine the multiple local pairwise correspondences to generate the local interimage relationship. Third, the interimage relationships of the GCM and LCM are integrated through a global-and-local correspondence aggregation (GLA) module to explore more comprehensive interimage collaboration cues. Finally, the intra and inter features are adaptively integrated by an intra-and-inter weighting fusion (AEWF) module to learn co-saliency features and predict the co-saliency map. The proposed GLNet is evaluated on three prevailing CoSOD benchmark datasets, demonstrating that our model trained on a small dataset (about 3k images) still outperforms 11 state-of-the-art competitors trained on some large datasets (about 8k-200k images).
关键词	Semantics Task analysis Feature extraction Convolution Object detection Computational modeling Collaborative work 3-D convolution co-salient object detection (CoSOD) global correspondence modeling (GCM) local correspondence modeling (LCM)
DOI	10.1109/TCYB.2022.3169431
收录类别	SCI
语种	英语
资助项目	National Key Research and Development Program of China[2021ZD0112100] ; Beijing Nova Program[Z201100006820016] ; National Natural Science Foundation of China[62002014] ; National Natural Science Foundation of China[U1936212] ; National Natural Science Foundation of China[62120106009] ; National Natural Science Foundation of China[U21B2038] ; National Natural Science Foundation of China[61931008] ; Beijing Natural Science Foundation[4222013] ; Hong Kong Research Grants Council (RGC) General Research Funds[9042816 (CityU 11209819)] ; Hong Kong Research Grants Council (RGC) General Research Funds[9042958 (CityU 11203820)] ; Hong Kong Innovation and Technology Commission (InnoHK Project CIMDA) ; China Association for Science and Technology[2020QNRC001] ; Beijing Association for Science and Technology ; Hong Kong Scholars Program[XJ2020040] ; Fundamental Research Funds for the Central Universities[2021YJS046] ; CAAI-Huawei MindSpore Open Fund
WOS研究方向	Automation & Control Systems ; Computer Science
WOS类目	Automation & Control Systems ; Computer Science, Artificial Intelligence ; Computer Science, Cybernetics
WOS记录号	WOS:000833060900001
出版者	IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
引用统计	被引频次：28[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/2XEOYT63/19505
专题	中国科学院计算技术研究所期刊论文_英文
通讯作者	Li, Chongyi
作者单位	1.Beijing Jiaotong Univ, Inst Informat Sci, Beijing 100044, Peoples R China 2.Beijing Jiaotong Univ, Beijing Key Lab Adv Informat Sci & Network Techno, Beijing 100044, Peoples R China 3.City Univ Hong Kong, Dept Comp Sci, Hong Kong, Peoples R China 4.Nanyang Technol Univ, Sch Comp Sci & Engn, Singapore, Singapore 5.ASTAR, Inst High Performance Comp, Singapore, Singapore 6.Univ Chinese Acad Sci, Sch Comp Sci & Technol, Beijing 101408, Peoples R China 7.Univ Chinese Acad Sci, Key Lab Big Data Min & Knowledge Management, Beijing 101408, Peoples R China 8.Chinese Acad Sci, Inst Comp Technol, Key Lab Intelligent Informat Proc, Beijing 100190, Peoples R China 9.Peng Cheng Lab, Shenzhen 518055, Peoples R China 10.City Univ Hong Kong, Dept Comp Sci, Hong Kong, Peoples R China 11.City Univ Hong Kong Shenzhen Res Inst, Shenzhen 51800, Peoples R China
推荐引用方式 GB/T 7714	Cong, Runmin,Yang, Ning,Li, Chongyi,et al. Global-and-Local Collaborative Learning for Co-Salient Object Detection[J]. IEEE TRANSACTIONS ON CYBERNETICS,2022:12.
APA	Cong, Runmin.,Yang, Ning.,Li, Chongyi.,Fu, Huazhu.,Zhao, Yao.,...&Kwong, Sam.(2022).Global-and-Local Collaborative Learning for Co-Salient Object Detection.IEEE TRANSACTIONS ON CYBERNETICS,12.
MLA	Cong, Runmin,et al."Global-and-Local Collaborative Learning for Co-Salient Object Detection".IEEE TRANSACTIONS ON CYBERNETICS (2022):12.