Self Supervised Action Recognition

Self-Supervised Action Recognition is a computer vision technique that automatically learns representations of actions in videos through unannotated data, aiming to enhance the model's understanding of complex dynamic scenes. This method leverages self-supervised learning mechanisms to extract features from large-scale unlabeled video data, optimizing the accuracy and robustness of action recognition. It is widely applied in fields such as intelligent surveillance, human-computer interaction, and behavioral analysis, possessing significant application value.