  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.
