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Home / Proceedings / Proceedings of the AAAI Conference on Artificial Intelligence, 36 / No. 2: AAAI-22 Technical Tracks 2

Self-Supervised Spatiotemporal Representation Learning by Exploiting Video Continuity

February 1, 2023

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Authors

Hanwen Liang

Huawei Noah's Ark Laboratory


Niamul Quader

Huawei Noah's Ark Laboratory


Zhixiang Chi

Huawei Noah's Ark Laboratory


Lizhe Chen

Huawei Noah's Ark Laboratory


Peng Dai

Huawei Noah's Ark Laboratory


Juwei Lu

Huawei Noah's Ark Laboratory


Yang Wang

Huawei Noah's Ark Laboratory University of Manitoba, Canada


DOI:

10.1609/aaai.v36i2.20047


Abstract:

Recent self-supervised video representation learning methods have found significant success by exploring essential properties of videos, e.g. speed, temporal order, etc. This work exploits an essential yet under-explored property of videos, the textit{video continuity}, to obtain supervision signals for self-supervised representation learning. Specifically, we formulate three novel continuity-related pretext tasks, i.e. continuity justification, discontinuity localization, and missing section approximation, that jointly supervise a shared backbone for video representation learning. This self-supervision approach, termed as Continuity Perception Network (CPNet), solves the three tasks altogether and encourages the backbone network to learn local and long-ranged motion and context representations. It outperforms prior arts on multiple downstream tasks, such as action recognition, video retrieval, and action localization. Additionally, the video continuity can be complementary to other coarse-grained video properties for representation learning, and integrating the proposed pretext task to prior arts can yield much performance gains.

Topics: AAAI

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HOW TO CITE:

Hanwen Liang||Niamul Quader||Zhixiang Chi||Lizhe Chen||Peng Dai||Juwei Lu||Yang Wang Self-Supervised Spatiotemporal Representation Learning by Exploiting Video Continuity Proceedings of the AAAI Conference on Artificial Intelligence (2022) 1564-1573.

Hanwen Liang||Niamul Quader||Zhixiang Chi||Lizhe Chen||Peng Dai||Juwei Lu||Yang Wang Self-Supervised Spatiotemporal Representation Learning by Exploiting Video Continuity AAAI 2022, 1564-1573.

Hanwen Liang||Niamul Quader||Zhixiang Chi||Lizhe Chen||Peng Dai||Juwei Lu||Yang Wang (2022). Self-Supervised Spatiotemporal Representation Learning by Exploiting Video Continuity. Proceedings of the AAAI Conference on Artificial Intelligence, 1564-1573.

Hanwen Liang||Niamul Quader||Zhixiang Chi||Lizhe Chen||Peng Dai||Juwei Lu||Yang Wang. Self-Supervised Spatiotemporal Representation Learning by Exploiting Video Continuity. Proceedings of the AAAI Conference on Artificial Intelligence 2022 p.1564-1573.

Hanwen Liang||Niamul Quader||Zhixiang Chi||Lizhe Chen||Peng Dai||Juwei Lu||Yang Wang. 2022. Self-Supervised Spatiotemporal Representation Learning by Exploiting Video Continuity. "Proceedings of the AAAI Conference on Artificial Intelligence". 1564-1573.

Hanwen Liang||Niamul Quader||Zhixiang Chi||Lizhe Chen||Peng Dai||Juwei Lu||Yang Wang. (2022) "Self-Supervised Spatiotemporal Representation Learning by Exploiting Video Continuity", Proceedings of the AAAI Conference on Artificial Intelligence, p.1564-1573

Hanwen Liang||Niamul Quader||Zhixiang Chi||Lizhe Chen||Peng Dai||Juwei Lu||Yang Wang, "Self-Supervised Spatiotemporal Representation Learning by Exploiting Video Continuity", AAAI, p.1564-1573, 2022.

Hanwen Liang||Niamul Quader||Zhixiang Chi||Lizhe Chen||Peng Dai||Juwei Lu||Yang Wang. "Self-Supervised Spatiotemporal Representation Learning by Exploiting Video Continuity". Proceedings of the AAAI Conference on Artificial Intelligence, 2022, p.1564-1573.

Hanwen Liang||Niamul Quader||Zhixiang Chi||Lizhe Chen||Peng Dai||Juwei Lu||Yang Wang. "Self-Supervised Spatiotemporal Representation Learning by Exploiting Video Continuity". Proceedings of the AAAI Conference on Artificial Intelligence, (2022): 1564-1573.

Hanwen Liang||Niamul Quader||Zhixiang Chi||Lizhe Chen||Peng Dai||Juwei Lu||Yang Wang. Self-Supervised Spatiotemporal Representation Learning by Exploiting Video Continuity. AAAI[Internet]. 2022[cited 2023]; 1564-1573.


ISSN: 2374-3468


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