Video action detection based on spatio-temporal contrastive learning

Human action detection in videos is crucial across various fields, yet manually annotating video data is time-consuming and results in limited datasets, weakening model generalization. To address this, this paper introduces a semi-supervised dual-channel spatio-temporal contrastive learning method-STCL, for accurate human action detection in videos. By constructing a dual-channel model to extract and compare spatio-temporal features in videos and adopting a semisupervised learning strategy that enhances unlabeled samples through forward and reverse playback, using a contrastive model to compare features, the method learns model parameters to distinguish between different video samples. This approach leverages the spatio-temporal information of unlabeled videos, avoiding extensive manual annotation. Experimental results on the Something-V2 dataset show that, even with only 5% labeled data, our method significantly outperforms existing techniques. This demonstrates the effectiveness of the proposed STCL model in utilizing video spatio-temporal information, achieving good results even with limited labeled data.