Weakly-Supervised Action Recognition

Weakly-Supervised Action Recognition (WSAR) in the field of computer vision involves identifying actions in videos using limited annotation information, such as single temporal point annotations instead of full temporal segment annotations. The task aims to enhance the model's generalization ability and data efficiency by reducing reliance on detailed annotations, thereby achieving more efficient action recognition on large-scale video datasets. Its application value lies in significantly reducing annotation costs, accelerating model training and deployment, and is suitable for multiple scenarios including surveillance, sports analysis, medical diagnosis, and more.