Semi Supervised Temporal Action Segmentation

Semi-supervised temporal action segmentation aims to automatically label each frame of a video with its corresponding action, using a limited amount of labeled data alongside a larger pool of unlabeled videos. Current research focuses on developing efficient algorithms, often employing temporal convolutional networks (TCNs) or transformers, that leverage both labeled and unlabeled data through techniques like contrastive learning and multi-level feature extraction to improve segmentation accuracy. This field is significant because it reduces the substantial annotation burden associated with fully supervised methods, enabling the development of more robust and scalable action recognition systems for applications in behavioral analysis, video understanding, and human-computer interaction.