| Title | Focal Visual-Text Attention for Visual Question Answering |
| Publication Type | Conference Paper |
| Year of Publication | 2018 |
| Authors | Liang, J., Jiang, L., Cao, L., Li, L., Hauptmann, A. |
| Conference Name | 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition |
| Keywords | Cognition, collective reasoning, composability, Computational modeling, Computer vision, Correlation, Focal Visual-Text Attention network, FVTA, inference mechanisms, Knowledge discovery, meta data, Metadata Discovery Problem, neural nets, Neural networks, pubcrawl, question answering (information retrieval), Resiliency, Scalability, single-image visual question answering, text analysis, text metadata, Videos, visual text sequence information, visualization |
| Abstract | 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 multimedia collections such as personal photos, 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 called Focal Visual-Text Attention network (FVTA) for collective reasoning in visual 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 MemexQA dataset and competitive results on the MovieQA dataset. |
| DOI | 10.1109/CVPR.2018.00642 |
| Citation Key | liang_focal_2018 |
Focal Visual-Text Attention for Visual Question Answering