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Vectorized Evidential Learning for Weakly-Supervised Temporal Action Localization
Gao, Junyu1,2; Chen, Mengyuan1,2; Xu, Changsheng1,2,3
Source PublicationIEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE
ISSN0162-8828
2023-12-01
Volume45Issue:12Pages:15949-15963
Corresponding AuthorXu, Changsheng(csxu@nlpr.ia.ac.cn)
AbstractWith the explosive growth of videos, weakly-supervised temporal action localization (WS-TAL) task has become a promising research direction in pattern analysis and machine learning. WS-TAL aims to detect and localize action instances with only video-level labels during training. Modern approaches have achieved impressive progress via powerful deep neural networks. However, robust and reliable WS-TAL remains challenging and underexplored due to considerable uncertainty caused by weak supervision, noisy evaluation environment, and unknown categories in the open world. To this end, we propose a new paradigm, named vectorized evidential learning (VEL), to explore local-to-global evidence collection for facilitating model performance. Specifically, a series of learnable meta-action units (MAUs) are automatically constructed, which serve as fundamental elements constituting diverse action categories. Since the same meta-action unit can manifest as distinct action components within different action categories, we leverage MAUs and category representations to dynamically and adaptively learn action components and action-component relations. After performing uncertainty estimation at both category-level and unit-level, the local evidence from action components is accumulated and optimized under the Subject Logic theory. Extensive experiments on the regular, noisy, and open-set settings of three popular benchmarks show that VEL consistently obtains more robust and reliable action localization performance than state-of-the-arts.
KeywordUncertainty Location awareness Reliability Videos Noise measurement Estimation Deep learning Weakly-supervised learning temporal action localization evidential deep learning uncertainty estimation
DOI10.1109/TPAMI.2023.3311447
WOS KeywordUNCERTAINTY
Indexed BySCI
Language英语
Funding ProjectNational Key Research and Development Plan of China[2020AAA0106200] ; National Natural Science Foundation of China[62036012] ; National Natural Science Foundation of China[62236008] ; National Natural Science Foundation of China[U21B2044] ; 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] ; National Natural Science Foundation of China[62002355] ; Beijing Natural Science Foundation[L201001] ; Open Research Projects of Zhejiang Lab[2022RC0AB02]
Funding OrganizationNational Key Research and Development Plan of China ; National Natural Science Foundation of China ; Beijing Natural Science Foundation ; Open Research Projects of Zhejiang Lab
WOS Research AreaComputer Science ; Engineering
WOS SubjectComputer Science, Artificial Intelligence ; Engineering, Electrical & Electronic
WOS IDWOS:001130146400117
PublisherIEEE COMPUTER SOC
Citation statistics
Document Type期刊论文
Identifierhttp://ir.ia.ac.cn/handle/173211/55540
Collection多模态人工智能系统全国重点实验室
Corresponding AuthorXu, Changsheng
Affiliation1.Chinese Acad Sci, Inst Automat, State Key Lab Multi modal Artificial Intelligence, Beijing 100190, Peoples R China
2.Univ Chinese Acad Sci, Sch Artificial Intelligence, Beijing 101408, Peoples R China
3.Peng Cheng Lab, Shenzhen 518055, Peoples R China
First Author AffilicationInstitute of Automation, Chinese Academy of Sciences
Corresponding Author AffilicationInstitute of Automation, Chinese Academy of Sciences
Recommended Citation
GB/T 7714
Gao, Junyu,Chen, Mengyuan,Xu, Changsheng. Vectorized Evidential Learning for Weakly-Supervised Temporal Action Localization[J]. IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE,2023,45(12):15949-15963.
APA Gao, Junyu,Chen, Mengyuan,&Xu, Changsheng.(2023).Vectorized Evidential Learning for Weakly-Supervised Temporal Action Localization.IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE,45(12),15949-15963.
MLA Gao, Junyu,et al."Vectorized Evidential Learning for Weakly-Supervised Temporal Action Localization".IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE 45.12(2023):15949-15963.
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