A number of recent works have proposed attention models for Visual Question Answering (VQA) that generate spatial maps highlighting image regions relevant to answering the question. In this paper, we argue that in addition to modeling "where to look" or visual attention, it is equally important to model "what words to listen to" or question attention. We present a novel co-attention model for VQA that jointly reasons about image and question attention. In addition, our model reasons about the question (and consequently the image via the co-attention mechanism) in a hierarchical fashion via a novel 1-dimensional convolution neural networks (CNN) model. Our final model outperforms all reported methods, improving the state-of-the-art on the VQA dataset from 60.4% to 62.1%, and from 61.6% to 65.4% on the COCO-QA dataset 1 . 1 The source code can be downloaded from https://github.com/jiasenlu/HieCoAttenVQA
Recent work also explores joint image and question based hierarchical co-attention REF .
Hierarchical Question-Image Co-Attention for Visual Question Answering
ArXiv
ArXiv
Computer Science