NODIS: Neural ordinary differential scene understanding

Semantic image understanding is a challenging topic in computer vision. It requires to detect all objects in an image, but also to identify all the relations between them. Detected objects, their labels and the discovered relations can be used to construct a scene graph which provides an abstract semantic interpretation of an image. In previous works, relations were identified by solving an assignment problem formulated as (Mixed-)Integer Linear Programs. In this work, we interpret that formulation as Ordinary Differential Equation (ODE). The proposed architecture performs scene graph inference by solving a neural variant of an ODE by end-to-end learning. The connection between (Mixed-)Integer Linear Program and ODEs in combination with the end-to-end training amounts to learning how to solve assignment problems with image-specific objective functions. Intuitive, visual explanations are provided for the role of the single free variable of the ODE modules which are associated with time in many natural processes. The proposed model achieves results equal to or above state-of-the-art on all three benchmark tasks: scene graph generation (SGGEN), classification (SGCLS) and visual relationship detection (PREDCLS) on Visual Genome benchmark. The strong results on scene graph classification support the claim that assignment problems can indeed be solved by neural ODEs.