D2C-Based Hybrid Network for Predicting Group Cohesion Scores

Abstract

Group cohesiveness represents the bonding between members in a group. Indeed, a group with high cohesiveness may easily reach success in their task. Therefore, the most critical element that affects the success of a group is group cohesiveness, which is estimated by Group Cohesion Score (GCS). This study proposed an automatic GCS estimation system for the 7th Emotion Recognition in the Wild (EmotiW 2019) challenge in the task of the Group Cohesion Prediction. We proposed a multi-stream hybrid network based on scene-level, skeleton-level, UV coordinates-level, mid-fusion, and face-level, followed by late-fusion to combine these approaches. We also developed a joint training method called Discrete labels to Continuous scores (D2C), where discrete labels (categorical labels) directly participate in generating continuous scores. Our proposed method achieved 0.416 mean squared error on the testing set of the EmotiW 2019 dataset and became a state-of-the-art in this challenge. Furthermore, to confirm the ability of the proposed D2C method, we performed experiments on the AffectNet database and obtained relatively better results than state-of-the-art approaches.

Keywords

• Group cohesion
• hybrid network
• discrete labels
• continuous labels
• valence/arousal