Learning Visual Commonsense for Robust Scene Graph Generation

Scene graph generation models understand the scene through object and predicate recognition, but are prone to mistakes due to the challenges of perception in the wild. Perception errors often lead to nonsensical compositions in the output scene graph, which do not follow real-world rules and patterns, and can be corrected using commonsense knowledge. We propose the first method to acquire visual commonsense such as affordance and intuitive physics automatically from data, and use that to enhance scene graph generation. To this end, we extend transformers to incorporate the structure of scene graphs, and train our Global-Local Attention Transformer on a scene graph corpus. Once trained, our commonsense model can be applied on any perception model and correct its obvious mistakes, resulting in a more commonsensical scene graph. We show the proposed model learns commonsense better than any alternative, and improves the accuracy of any scene graph generation model. Nevertheless, strong disproportions in real-world datasets could bias commonsense to miscorrect already confident perceptions. We address this problem by devising a fusion module that compares predictions made by the perception and commonsense models, and the confidence of each, to make a hybrid decision. Our full model learns commonsense and knows when to use it, which is shown effective through experiments, resulting in a new state of the art.