Hierarchical Question-Image Co-Attention for Visual Question Answering
This work addresses the challenge of accurately answering questions about images for applications in AI and vision systems, representing an incremental improvement over existing attention models.
The paper tackled the problem of Visual Question Answering by proposing a hierarchical co-attention model that jointly reasons about image and question attention, improving state-of-the-art accuracy from 60.3% to 60.5% on the VQA dataset and from 61.6% to 63.3% on the COCO-QA dataset, with further gains using ResNet.
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.