Focal Visual-Text Attention for Visual Question Answering

Junwei Liang
Lu Jiang
Liangliang Cao
Jia Li
Alexander Hauptmann


Recent insights on language and vision with neural networks have been successfully applied to simple single-image visual question answering. However, to tackle real-life question answering problems on personal collections, we have to look at whole collections with sequences of photos or videos. When answering questions from a large collection, a natural problem is to identify snippets to support the answer. In this paper, we describe a novel neural network model called Focal Visual-Text Attention network (FVTA) for collective reasoning in personalized question answering, where both visual and text sequence information such as images and text metadata are presented. FVTA introduces an end-to-end approach that makes use of a hierarchical process to dynamically determine what media and what time to focus on in the sequential data to answer the question. FVTA can not only answer the questions well but also provides the justifications which the system results are based upon to get the answers. FVTA achieves state-of-the-art performance on the newly released MemexQA dataset and the MovieQA dataset.