TSkel-Mamba: Temporal Dynamic Modeling via State Space Model for Human Skeleton-based Action Recognition
PositiveArtificial Intelligence
- The TSkel-Mamba framework has been introduced to enhance skeleton-based action recognition by integrating a hybrid Transformer-Mamba approach, which captures both spatial and temporal dynamics effectively. This model utilizes a new Temporal Dynamic Modeling block and a Multi-scale Temporal Interaction module to improve the recognition of human actions from skeleton data.
- This development is significant as it addresses the limitations of previous models like Mamba, particularly in modeling inter-channel dependencies, thereby improving the accuracy and robustness of action recognition systems in various applications, including surveillance and human-computer interaction.
- The introduction of TSkel-Mamba aligns with ongoing advancements in AI and machine learning, particularly in the realm of skeleton-based action recognition. It reflects a broader trend towards integrating different modeling techniques, such as Transformers and state-space models, to enhance performance across various domains, including visual recognition and natural language processing.
— via World Pulse Now AI Editorial System
