Multi Label Action

Multi-label action recognition focuses on identifying multiple actions occurring simultaneously within a video, a complex task due to overlapping actions and temporal dependencies. Current research emphasizes improving the accuracy and efficiency of models, often employing transformer-based architectures enhanced with techniques like relative positional encoding to better capture temporal information and handle co-occurrence relationships between actions. This field is crucial for advancing video understanding in robotics, human-computer interaction, and sports analytics, where accurately interpreting complex actions is essential for effective applications.