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: this https URL

提出了一种名为 MAGIC-TBR 的多视角注意力融合方法，通过基于变压器的方法结合从视频中提取的特征和相应的离散余弦变换系数，探索细节行为（如手势、整理或笨拙）的检测。在 BBSI 数据集上进行了实验，结果证明了所提特征融合与多视角注意力的有效性。