The study of algorithms to automatically answer visual questions currently is motivated by
visual question answering (VQA) datasets constructed in artificial VQA settings. We propose …

We propose the task of free-form and open-ended Visual Question Answering (VQA). Given
an image and a natural language question about the image, the task is to provide an …

In visual question answering (VQA), an algorithm must answer text-based questions about
images. While multiple datasets for VQA have been created since late 2014, they all have …

Problems at the intersection of vision and language are of significant importance both as
challenging research questions and for the rich set of applications they enable. However …

A number of studies have found that today's Visual Question Answering (VQA) models are
heavily driven by superficial correlations in the training data and lack sufficient image …

Abstract Visual Question Answering (VQA) research is split into two camps: the first focuses
on VQA datasets that require natural image understanding and the second focuses on …

We present a method that learns to answer visual questions by selecting image regions
relevant to the text-based query. Our method maps textual queries and visual features from …

Abstract Visual Question Answering (VQA) has emerged as an important problem spanning
Computer Vision, Natural Language Processing and Artificial Intelligence (AI). In …

Recently, algorithms for object recognition and related tasks have become sufficiently
proficient that new vision tasks can now be pursued. In this paper, we build a system …

In this paper, we exploit memory-augmented neural networks to predict accurate answers to
visual questions, even when those answers rarely occur in the training set. The memory …