MAGIC-TBR: Multiview Attention Fusion for Transformer-based Bodily
  Behavior Recognition in Group Settings
Surbhi Madan Rishabh Jain Gulshan Sharma Ramanathan Subramanian Abhinav Dhall
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