Recurrent Visual Relationship Recognition with Triplet Unit

Abstract

The task of visual relationship recognition (VRR) is recognizing multiple objects and their relationships in an image. A fundamental difficulty of this task is class-number scalability, since the number of possible relationships we need to consider causes combinatorial explosion. Another difficulty of this task is modeling how to avoid outputting semantically redundant relationships. To overcome these challenges, this paper proposes a novel architecture with a recurrent neural network (RNN) and triplet unit (TU). The RNN allows our model to be optimized for outputting a sequence of relationships. By optimizing our model to a semantically diverse relationship sequence, we increase the variety in output relationships. At each step of the RNN, our TU enables the model to classify a relationship while achieving class-number scalability by decomposing a relationship into a subject-predicate-object (SPO) triplet. We evaluate our model on various datasets and compare the results to a baseline. These experimental results show our model's superior recall and precision with fewer predictions compared to the baseline, even as it produces greater variety in relationships.