Abstract

This paper proposes to improve visual question answering (VQA) with structured representations of both scene contents and questions. A key challenge in VQA is to require joint reasoning over the visual and text domains. The predominant CNN/LSTM-based approach to VQA is limited by monolithic vector representations that largely ignore structure in the scene and in the question. CNN feature vectors cannot effectively capture situations as simple as multiple object instances, and LSTMs process questions as series of words, which do not reflflect the true complexity of language structure. We instead propose to build graphs over the scene objects and over the question words, and we describe a deep neural network that exploits the structure in these representations. We show that this approach achieves signifificant improvements over the state-of-the-art, increasing accuracy from 71.2% to 74.4% on the “abstract scenes” multiple-choice benchmark, and from 34.7% to 39.1% for the more challenging “balanced” scenes, i.e. image pairs with fifine-grained differences and opposite yes/no answers to a same question.