Contextual Translation Embedding for Visual Relationship Detection and Scene Graph Generation.

Relations amongst entities play a central role in image understanding. Due to the complexity of modeling (subject, predicate, object) relation triplets, it is crucial to develop a method that can not only recognize seen relations, but also generalize to unseen cases. Inspired by a previously proposed visual translation embedding model, or VTransE [1] , we propose a context-augmented translation embedding model that can capture both common and rare relations.