Visual Question Answering on CLEVR Dataset via Multimodal Fusion and Relational Reasoning

Visual question answering (VQA) is a new interdisciplinary research area, in which a model attempts to answer a free-form question in natural language based on a given image. In this paper we improve an existing model, which leverages relational networks and attention mechanism, by decreasing its train and inference time while preserving a comparable accuracy.