Is alice chasing or being chased?: Determining subject and object of activities in videos
Recent progress in video description has shown promising results by combining object/action recognition and natural language processing techniques. However, even the most simplest form of the generated sentence, the SVO triplet (Subject/Verb/Object), can be misleading for its lack of role relationship analysis. When the system detects keywords "person", "baby" and "feed", we do not want the system to generate "a person feeding a baby" when the actual screen is a scene where the baby is trying to share the food. In this paper, we explore role relationships between objects/persons and their usage in generating a more meaningful video description. More specifically, we confine ourselves on the following problem: identifying subject and object roles in two-person activities. We argue that the subject and object roles have consistent properties across different activities. To that end, we cast this problem as a domain adaptation problem. A novel Youtube SVO dataset is proposed for evaluating methods developed for this problem. The performance of the proposed method is compared against several baseline methods.