MAGIC-TBR: Multiview Attention Fusion for Transformer-based Bodily Behavior Recognition in Group Settings
This work addresses the need for automated analysis of subtle social cues in AI systems, but it appears incremental as it builds on existing transformer and attention methods for a specific domain.
The paper tackled the problem of detecting finer bodily behaviors like gesturing and grooming in group settings by proposing MAGIC-TBR, a multiview attention fusion method using transformers, and demonstrated its effectiveness on the BBSI dataset.
Bodily behavioral language is an important social cue, and its automated analysis helps in enhancing the understanding of artificial intelligence systems. Furthermore, behavioral language cues are essential for active engagement in social agent-based user interactions. Despite the progress made in computer vision for tasks like head and body pose estimation, there is still a need to explore the detection of finer behaviors such as gesturing, grooming, or fumbling. This paper proposes a multiview attention fusion method named MAGIC-TBR that combines features extracted from videos and their corresponding Discrete Cosine Transform coefficients via a transformer-based approach. The experiments are conducted on the BBSI dataset and the results demonstrate the effectiveness of the proposed feature fusion with multiview attention. The code is available at: https://github.com/surbhimadan92/MAGIC-TBR