  Group affect refers to the subjective emotion that is evoked by an external
stimulus in a group, which is an important factor that shapes group behavior
and outcomes. Recognizing group affect involves identifying important
individuals and salient objects among a crowd that can evoke emotions. However,
most existing methods lack attention to affective meaning in group dynamics and
fail to account for the contextual relevance of faces and objects in
group-level images. In this work, we propose a solution by incorporating the
psychological concept of the Most Important Person (MIP), which represents the
most noteworthy face in a crowd and has affective semantic meaning. We present
the Dual-branch Cross-Patch Attention Transformer (DCAT) which uses global
image and MIP together as inputs. Specifically, we first learn the informative
facial regions produced by the MIP and the global context separately. Then, the
Cross-Patch Attention module is proposed to fuse the features of MIP and global
context together to complement each other. Our proposed method outperforms
state-of-the-art methods on GAF 3.0, GroupEmoW, and HECO datasets. Moreover, we
demonstrate the potential for broader applications by showing that our proposed
model can be transferred to another group affect task, group cohesion, and
achieve comparable results.
