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Uncertainty-Aware Dual-Evidential Learning for Weakly-Supervised Temporal Action Localization
Chen, Mengyuan1,2; Gao, Junyu1,2; Xu, Changsheng1,2,3
Source PublicationIEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE
ISSN0162-8828
2023-12-01
Volume45Issue:12Pages:15896-15911
Corresponding AuthorXu, Changsheng(csxu@nlpr.ia.ac.cn)
AbstractWeakly-supervised temporal action localization (WTAL) aims to localize the action instances and recognize their categories with only video-level labels. Despite great progress, existing methods suffer from severe action-background ambiguity, which mainly arises from background noise and neglect of non-salient action snippets. To address this issue, we propose a generalized evidential deep learning (EDL) framework forWTAL, called Uncertainty-aware Dual-Evidential Learning (UDEL), which extends the traditional paradigm of EDL to adapt to the weakly-supervised multi-label classification goal with the guidance of epistemic and aleatoric uncertainties, of which the former comes from models lacking knowledge, while the latter comes from the inherent properties of samples themselves. Specifically, targeting excluding the undesirable background snippets, we fuse the video-level epistemic and aleatoric uncertainties to measure the interference of background noise to video-level prediction. Then, the snippet-level aleatoric uncertainty is further deduced for progressive mutual learning, which gradually focuses on the entire action instances in an "easy-to-hard" manner and encourages the snippet-level epistemic uncertainty to be complementary with the foreground attention scores. Extensive experiments show that UDEL achieves state-of-the-art performance on four public benchmarks. Our code is available in github/mengyuanchen2021/ UDEL.
KeywordUncertainty Background noise Task analysis Location awareness Measurement uncertainty Interference Predictive models Weakly-supervised temporal action localization evidential deep learning uncertainty estimation
DOI10.1109/TPAMI.2023.3308571
WOS KeywordATTENTION
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[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] ; 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:001130146400114
PublisherIEEE COMPUTER SOC
Citation statistics
Document Type期刊论文
Identifierhttp://ir.ia.ac.cn/handle/173211/55494
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 Multimodal 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
Chen, Mengyuan,Gao, Junyu,Xu, Changsheng. Uncertainty-Aware Dual-Evidential Learning for Weakly-Supervised Temporal Action Localization[J]. IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE,2023,45(12):15896-15911.
APA Chen, Mengyuan,Gao, Junyu,&Xu, Changsheng.(2023).Uncertainty-Aware Dual-Evidential Learning for Weakly-Supervised Temporal Action Localization.IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE,45(12),15896-15911.
MLA Chen, Mengyuan,et al."Uncertainty-Aware Dual-Evidential Learning for Weakly-Supervised Temporal Action Localization".IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE 45.12(2023):15896-15911.
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