Visual Question Answering (VQA) is an important task in multimodal AI, and it is often used to test the ability of vision-language models to understand and reason on knowledge present in both visual and textual data.

There is a recent focus on designing architectures that have an Integer Linear Programming (ILP) layer following a neural model (referred to asNeural ILP in this paper).

Visual Reinforcement Learning (Visual RL), coupled with high-dimensional observations, has consistently confronted the long-standing challenge of out-of-distribution generalization.