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Dual-Evidential Learning for Weakly-supervised Temporal Action Localization

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Computer Vision – ECCV 2022 (ECCV 2022)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13664))

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Abstract

Weakly-supervised temporal action localization (WS-TAL) 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 comes from background noise introduced by aggregation operations and large intra-action variations caused by the task gap between classification and localization. To address this issue, we propose a generalized evidential deep learning (EDL) framework for WS-TAL, called Dual-Evidential Learning for Uncertainty modeling (DELU), which extends the traditional paradigm of EDL to adapt to the weakly-supervised multi-label classification goal. Specifically, targeting at adaptively excluding the undesirable background snippets, we utilize the video-level uncertainty to measure the interference of background noise to video-level prediction. Then, the snippet-level uncertainty is further deduced for progressive learning, which gradually focuses on the entire action instances in an “easy-to-hard” manner. Extensive experiments show that DELU achieves state-of-the-art performance on THUMOS14 and ActivityNet1.2 benchmarks. Our code is available in github.com/MengyuanChen21/ECCV2022-DELU.

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Notes

  1. 1.

    In WS-TAL, multiple types of action may appear simultaneously in a video.

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Acknowledgements

This work was supported by the National Key Research & Development Plan of China under Grant 2020AAA0106200, in part by the National Natural Science Foundation of China under Grants 62036012, U21B2044, 61721004, 62102415, 62072286, 61720106006, 61832002, 62072455, 62002355, and U1836220, in part by Beijing Natural Science Foundation (L201001), in part by Open Research Projects of Zhejiang Lab (NO.2022RC0AB02), and in part by CCF-Hikvision Open Fund (20210004).

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Authors and Affiliations

  1. National Lab of Pattern Recognition (NLPR), Institute of Automation, Chinese Academy of Sciences (CASIA), Beijing, China

    Mengyuan Chen, Junyu Gao & Changsheng Xu

  2. School of Artificial Intelligence, University of Chinese Academy of Sciences (UCAS), Beijing, China

    Mengyuan Chen, Junyu Gao & Changsheng Xu

  3. Hikvision Research Institute, Hangzhou, China

    Shicai Yang

  4. Peng Cheng Laboratory, ShenZhen, China

    Changsheng Xu

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  1. Mengyuan Chen
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  2. Junyu Gao
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  4. Changsheng Xu
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Correspondence to Mengyuan Chen .

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Editors and Affiliations

  1. Tel Aviv University, Tel Aviv, Israel

    Shai Avidan

  2. University College London, London, UK

    Gabriel Brostow

  3. Google AI, Accra, Ghana

    Moustapha Cissé

  4. University of Catania, Catania, Italy

    Giovanni Maria Farinella

  5. Facebook (United States), Menlo Park, CA, USA

    Tal Hassner

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Chen, M., Gao, J., Yang, S., Xu, C. (2022). Dual-Evidential Learning for Weakly-supervised Temporal Action Localization. In: Avidan, S., Brostow, G., Cissé, M., Farinella, G.M., Hassner, T. (eds) Computer Vision – ECCV 2022. ECCV 2022. Lecture Notes in Computer Science, vol 13664. Springer, Cham. https://doi.org/10.1007/978-3-031-19772-7_12

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