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Interactions between people are often governed by their relationships. On theflip side, social relationships are built upon several interactions. Twostrangers are more likely to greet and introduce themselves while becomingfriends over time. We are fascinated by this interplay between interactions andrelationships, and believe that it is an important aspect of understandingsocial situations. In this work, we propose neural models to learn and jointlypredict interactions, relationships, and the pair of characters that areinvolved. We note that interactions are informed by a mixture of visual anddialog cues, and present a multimodal architecture to extract meaningfulinformation from them. Localizing the pair of interacting characters in videois a time-consuming process, instead, we train our model to learn fromclip-level weak labels. We evaluate our models on the MovieGraphs dataset andshow the impact of modalities, use of longer temporal context for predictingrelationships, and achieve encouraging performance using weak labels ascompared with ground-truth labels. Code is online.

Learning Interactions and Relationships between Movie Characters