Iterative Collective Classification for Visual Focus of Attention Prediction

Identifying the visual focus of attention (VFOA) in multi-person discussions is related to many types of social interactions such as dominance/deference, like/dislike, and trust/distrust relationships. However, identifying the VFOA of a person is challenging since it changes rapidly in dynamic discussions. We propose ICAF (Iterative Collective Attention Focus), a system that simultaneously tracks the VFOA and speaking probabilities of all people. In order to apply the system previously unseen videos, we propose a lightly supervised technique to train the model in ICAF with performance which is only slightly worse than in the fully supervised case. Our system can visualize the predicted VFOA and speaking probabilities and interaction networks in videos.