Semantic and Temporal Contextual Correlation Learning for Weakly-Supervised Temporal Action Localization

doi:10.1109/TPAMI.2023.3287208

	Semantic and Temporal Contextual Correlation Learning for Weakly-Supervised Temporal Action Localization
	Fu, Jie 1,2; Gao, Junyu2,3 ; Xu, Changsheng2,3,4
发表期刊	IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE
ISSN	0162-8828
	2023-10-01
卷号	45 期号:10 页码:12427-12443
通讯作者	Xu, Changsheng(csxu@nlpr.ia.ac.cn)
摘要	Weakly-supervised temporal action localization (WSTAL) aims to automatically identify and localize action instances in untrimmed videos with only video-level labels as supervision. In this task, there exist two challenges: (1) how to accurately discover the action categories in an untrimmed video (what to discover); (2) how to elaborately focus on the integral temporal interval of each action instance (where to focus). Empirically, to discover the action categories, discriminative semantic information should be extracted, while robust temporal contextual information is beneficial for complete action localization. However, most existing WSTAL methods ignore to explicitly and jointly model the semantic and temporal contextual correlation information for the above two challenges. In this article, a Semantic and Temporal Contextual Correlation Learning Network (STCL-Net) with the semantic (SCL) and temporal contextual correlation learning (TCL) modules is proposed, which achieves both accurate action discovery and complete action localization by modeling the semantic and temporal contextual correlation information for each snippet in the inter- and intra-video manners respectively. It is noteworthy that the two proposed modules are both designed in a unified dynamic correlation-embedding paradigm. Extensive experiments are performed on different benchmarks. On all the benchmarks, our proposed method exhibits superior or comparable performance in comparison to the existing state-of-the-art models, especially achieving gains as high as 7.2% in terms of the average mAP on THUMOS-14. In addition, comprehensive ablation studies also verify the effectiveness and robustness of each component in our model.
关键词	Weakly-supervised video action localization semantic temporal context correlation learning
DOI	10.1109/TPAMI.2023.3287208
收录类别	SCI
语种	英语
资助项目	National Key Research and Development Plan of China ; National Natural Science Foundation of China[2020AAA0106200] ; National Natural Science Foundation of China[62036012] ; National Natural Science Foundation of China[U21B2044] ; National Natural Science Foundation of China[62236008] ; National Natural Science Foundation of China[61721004] ; National Natural Science Foundation of China[62102415] ; National Natural Science Foundation of China[62072286] ; National Natural Science Foundation of China[62106262] ; Beijing Natural Science Foundation[62002355] ; Open Research Projects of Zhejiang Lab[L201001] ; [2022RC0AB02]
项目资助者	National Key Research and Development Plan of China ; National Natural Science Foundation of China ; Beijing Natural Science Foundation ; Open Research Projects of Zhejiang Lab
WOS研究方向	Computer Science ; Engineering
WOS类目	Computer Science, Artificial Intelligence ; Engineering, Electrical & Electronic
WOS记录号	WOS:001068816800057
出版者	IEEE COMPUTER SOC
引用统计	被引频次：1[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	http://ir.ia.ac.cn/handle/173211/53045
专题	多模态人工智能系统全国重点实验室
通讯作者	Xu, Changsheng
作者单位	1.Zhengzhou Univ, Zhengzhou 450001, Henan, Peoples R China 2.Chinese Acad Sci, Inst Automat, State Key Lab Multimodal Artificial Intelligence S, Beijing 100190, Peoples R China 3.Univ Chinese Acad Sci, Sch Artificial Intelligence, Beijing 101408, Peoples R China 4.Peng Cheng Lab, Shenzhen 518055, Guangdong, Peoples R China
第一作者单位	中国科学院自动化研究所
通讯作者单位	中国科学院自动化研究所
推荐引用方式 GB/T 7714	Fu, Jie,Gao, Junyu,Xu, Changsheng. Semantic and Temporal Contextual Correlation Learning for Weakly-Supervised Temporal Action Localization[J]. IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE,2023,45(10):12427-12443.
APA	Fu, Jie,Gao, Junyu,&Xu, Changsheng.(2023).Semantic and Temporal Contextual Correlation Learning for Weakly-Supervised Temporal Action Localization.IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE,45(10),12427-12443.
MLA	Fu, Jie,et al."Semantic and Temporal Contextual Correlation Learning for Weakly-Supervised Temporal Action Localization".IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE 45.10(2023):12427-12443.