Contrastive Spatio-Temporal Pretext Learning for Self-supervised Video Representation

Research output: Chapters, Conference Papers, Creative and Literary Works (RGC: 12, 32, 41, 45)32_Refereed conference paper (with ISBN/ISSN)peer-review

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Detail(s)

Original languageEnglish
Title of host publicationProceedings of the 36th AAAI Conference on Artificial Intelligence (AAAI'2022)
PublisherAAAI Press
Pages3380-3389
Volume36
ISBN (Print)978-1-57735-876-3
Publication statusPublished - 30 Jun 2022

Conference

Title36th AAAI Conference on Artificial Intelligence (AAAI-22)
LocationVirtual Conference
Period22 February - 1 March 2022

Abstract

Spatio-temporal representation learning is critical for video self-supervised representation. Recent approaches mainly use contrastive learning and pretext tasks. However, these approaches learn representation by discriminating sampled instances via feature similarity in the latent space while ignoring the intermediate state of the learned representations, which limits the overall performance. In this work, taking into account the degree of similarity of sampled instances as the intermediate state, we propose a novel pretext task - spatiotemporal overlap rate (STOR) prediction. It stems from the observation that humans are capable of discriminating the overlap rates of videos in space and time. This task encourages the model to discriminate the STOR of two generated samples to learn the representations. Moreover, we employ a joint optimization combining pretext tasks with contrastive learning to further enhance the spatio-temporal representation learning. We also study the mutual influence of each component in the proposed scheme. Extensive experiments demonstrate that our proposed STOR task can favor both contrastive learning and pretext tasks. The joint optimization scheme can significantly improve the spatio-temporal representation in video understanding. The code is available at https://github.com/Katou2/CSTP. 

Citation Format(s)

Contrastive Spatio-Temporal Pretext Learning for Self-supervised Video Representation. / Zhang, Yujia; Po, Lai-Man; Xu, Xuyuan et al.

Proceedings of the 36th AAAI Conference on Artificial Intelligence (AAAI'2022). Vol. 36 AAAI Press, 2022. p. 3380-3389.

Research output: Chapters, Conference Papers, Creative and Literary Works (RGC: 12, 32, 41, 45)32_Refereed conference paper (with ISBN/ISSN)peer-review