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

arXiv:2404.16416v1 Announce Type: new
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 …

abstract action recognition arxiv cs.cv data embodied making predictions reasoning recognition semi-supervised temporal type