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.

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

The paper presents an incremental contribution with respect to previous methods for VQA that only exploit an image attention mechanism guided by question data. Here, they also consider a question attention mechanism guided by image information. In this sense, the main hypothesis of this work is that jointly considering visual and question attention mechanisms can improve the performance of current VQA systems. I agree that this hypothesis can be relevant for the case of long questions, but I believe there is also a risk that question based attention guided by image information can be misleading, in the sense that usually an image includes several information sources, while the question is more focused. In Figure 3, authors include a graph that shows the impact of question length in performance, while this figure seems to show a tendency, the effect is still weak, maybe a numerical analysis can help to support this point. I believe, an analysis of potential differences (not only question length) between most common errors of previous works (only image attention) and the proposed approach (image and question attention) can help to support the relevance of the proposed attention mechanism.

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.

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). Our model improves the state-of-the-art on the VQA dataset from 60.3% to 60.5%, and from 61.6% to 63.3% on the COCO-QA dataset. By using ResNet, the performance is further improved to 62.1% for VQA and 65.4% for COCO-QA.