Semi Supervised Human Pose Estimation

Semi-supervised human pose estimation aims to combine unannotated data with annotated data to enhance model performance. This task leverages the structural information in a large number of unannotated images, improving the model's ability to recognize human poses in complex scenarios. As a result, it can increase the accuracy and robustness of computer vision systems while reducing annotation costs. Its application value lies in its broad applicability to areas such as action recognition, behavior analysis, and virtual reality, driving the development and innovation of related technologies.