  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. It achieves
state-of-the-art results on all three benchmark tasks: scene graph generation
(SGGen), classification (SGCls) and visual relationship detection (PredCls) on
Visual Genome benchmark.
