Deep Modular Co-Attention Networks for Visual Question Answering

作者：Yu Zhou, Jun Yu, Yuhao Cui, Dacheng Tao, Qi Tian · 年份：2019 · DOI：10.1109/cvpr.2019.00644 · 被引用次数：958 · 研究领域：Multimodal Machine Learning Applications、Advanced Image and Video Retrieval Techniques、Domain Adaptation and Few-Shot Learning

Visual Question Answering (VQA) requires a fine-grained and simultaneous understanding of both the visual content of images and the textual content of questions. Therefore, designing an effective `co-attention' model to associate key words in questions with key objects in images is central to VQA performance. So far, most successful attempts at co-attention learning have been achieved by using shallow models, and deep co-attention models show little improvement over their shallow counterparts. In this paper, we propose a deep Modular Co-Attention Network (MCAN) that consists of Modular Co-Attention (MCA) layers cascaded in depth. Each MCA layer models the self-attention of questions and images, as well as the question-guided-attention of images jointly using a modular composition of two basic attention units. We quantitatively and qualitatively evaluate MCAN on the benchmark VQA-v2 dataset and conduct extensive ablation studies to explore the reasons behind MCAN's effectiveness. Experimental results demonstrate that MCAN significantly outperforms the previous state-of-the-art. Our best single model delivers 70.63% overall accuracy on the test-dev set.