FVQA: Fact-based Visual Question Answering

TL;DR

FVQA introduces a new dataset for visual question answering that requires external factual knowledge and reasoning with supporting facts, advancing the capability of models to perform deeper understanding beyond direct image-question analysis.

Contribution

The paper presents FVQA, a dataset that emphasizes questions needing external facts and supports reasoning with structured supporting fact triplets, along with baseline and novel models.

Findings

Baseline models perform modestly on FVQA.

The proposed model effectively utilizes supporting facts for reasoning.

FVQA advances VQA towards external knowledge integration.

Abstract

Visual Question Answering (VQA) has attracted a lot of attention in both Computer Vision and Natural Language Processing communities, not least because it offers insight into the relationships between two important sources of information. Current datasets, and the models built upon them, have focused on questions which are answerable by direct analysis of the question and image alone. The set of such questions that require no external information to answer is interesting, but very limited. It excludes questions which require common sense, or basic factual knowledge to answer, for example. Here we introduce FVQA, a VQA dataset which requires, and supports, much deeper reasoning. FVQA only contains questions which require external information to answer. We thus extend a conventional visual question answering dataset, which contains image-question-answerg triplets, through additional image-question-answer-supporting fact tuples. The supporting fact is represented as a structural triplet, such as <Cat,CapableOf,ClimbingTrees>. We evaluate several baseline models on the FVQA dataset, and describe a novel model which is capable of reasoning about an image on the basis of supporting facts.