Learning Discriminative Spatio-temporal Representations for Semi-supervised Action Recognition

• URL: http://arxiv.org/abs/2404.16416v1
• Date: Thu, 25 Apr 2024 08:49:08 GMT
• Title: Learning Discriminative Spatio-temporal Representations for Semi-supervised Action Recognition
• Authors: Yu Wang, Sanping Zhou, Kun Xia, Le Wang,
• Abstract summary: We propose an Adaptive Contrastive Learning(ACL) strategy and a Multi-scale Temporal Learning(MTL) strategy. ACL strategy assesses the confidence of all unlabeled samples by the class prototypes of the labeled data, and adaptively selects positive-negative samples from a pseudo-labeled sample bank to construct contrastive learning. MTL strategy could highlight informative semantics from long-term clips and integrate them into the short-term clip while suppressing noisy information.
• Score: 23.44320273156057
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Semi-supervised action recognition aims to improve spatio-temporal reasoning ability with a few labeled data in conjunction with a large amount of unlabeled data. Albeit recent advancements, existing powerful methods are still prone to making ambiguous predictions under scarce labeled data, embodied as the limitation of distinguishing different actions with similar spatio-temporal information. In this paper, we approach this problem by empowering the model two aspects of capability, namely discriminative spatial modeling and temporal structure modeling for learning discriminative spatio-temporal representations. Specifically, we propose an Adaptive Contrastive Learning~(ACL) strategy. It assesses the confidence of all unlabeled samples by the class prototypes of the labeled data, and adaptively selects positive-negative samples from a pseudo-labeled sample bank to construct contrastive learning. Additionally, we introduce a Multi-scale Temporal Learning~(MTL) strategy. It could highlight informative semantics from long-term clips and integrate them into the short-term clip while suppressing noisy information. Afterwards, both of these two new techniques are integrated in a unified framework to encourage the model to make accurate predictions. Extensive experiments on UCF101, HMDB51 and Kinetics400 show the superiority of our method over prior state-of-the-art approaches.

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