Hierarchical Co-Attention for Visual Question Answering
Jiasen Lu, Jianwei Yang, Dhruv Batra, Devi Parikh · arXiv (Cornell University) · 2016
A number of recent works have proposed attention models for Visual Question Answering (VQA) that generate spatial maps highlighting image regions relevant answering the question. In this paper, we argue that in addition modeling where look or visual attention, it is equally important model what words 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% 62.1%, and from 61.6% 65.4% on the COCO-QA dataset.