Bilateral Cross-Modality Graph Matching Attention for Feature Fusion in Visual Question Answering
This addresses the problem of cross-modality feature fusion for VQA researchers, though it appears incremental as it builds on existing graph-based methods.
The paper tackles the challenge of aligning visual and textual features in Visual Question Answering (VQA) by proposing a Graph Matching Attention (GMA) network, which achieves state-of-the-art performance on the GQA and VQA 2.0 datasets.
Answering semantically-complicated questions according to an image is challenging in Visual Question Answering (VQA) task. Although the image can be well represented by deep learning, the question is always simply embedded and cannot well indicate its meaning. Besides, the visual and textual features have a gap for different modalities, it is difficult to align and utilize the cross-modality information. In this paper, we focus on these two problems and propose a Graph Matching Attention (GMA) network. Firstly, it not only builds graph for the image, but also constructs graph for the question in terms of both syntactic and embedding information. Next, we explore the intra-modality relationships by a dual-stage graph encoder and then present a bilateral cross-modality graph matching attention to infer the relationships between the image and the question. The updated cross-modality features are then sent into the answer prediction module for final answer prediction. Experiments demonstrate that our network achieves state-of-the-art performance on the GQA dataset and the VQA 2.0 dataset. The ablation studies verify the effectiveness of each modules in our GMA network.