Self Supervised Human Action Recognition

Self-Supervised Human Action Recognition is a computer vision technique aimed at automatically learning feature representations of human actions through unannotated data, thereby achieving efficient recognition and classification of complex actions. This method leverages a self-supervised learning framework to extract useful information from large-scale unlabeled videos, enhancing the model's generalization and robustness. It is widely applied in areas such as intelligent surveillance, human-computer interaction, and motion analysis.