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Multimodal Bi-direction Guided Attention Networks for Visual Question Answering

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Abstract

Current visual question answering (VQA) has become a research hotspot in the computer vision and natural language processing field. A core solution of VQA is how to fuse multi-modal features from images and questions. This paper proposes a Multimodal Bi-direction Guided Attention Network (MBGAN) for VQA by combining visual relationships and attention to achieve more refined feature fusion. Specifically, the self-attention is used to extract image features and text features, the guided-attention is applied to obtain the correlation between each image area and the related question. To obtain the relative position relationship of different objects, position attention is further introduced to realize relationship correlation modeling and enhance the matching ability of multi-modal features. Given an image and a natural language question, the proposed MBGAN learns visual relation inference and question attention networks in parallel to achieve the fine-grained fusion of the visual features and the textual features, then the final answers can be obtained accurately through model stacking. MBGAN achieves 69.41% overall accuracy on the VQA-v1 dataset, 70.79% overall accuracy on the VQA-v2 dataset, and 68.79% overall accuracy on the COCO-QA dataset, which shows that the proposed MBGAN outperforms most of the state-of-the-art models.

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Acknowledgements

This work is funded by the National Natural Science Foundation of China (62277008) and the Educational Informatization Project of Chongqing University of Posts and Telecommunications (xxhyf2022-08).

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Authors and Affiliations

  1. Research Center for Artificial Intelligence and Smart Education, Chongqing University of Posts and Telecommunications, Chongqing, 400065, China

    Linqin Cai, Nuoying Xu, Hang Tian & Haodu Fan

  2. Chengdu Huawei Technology Co., Ltd, Chengdu, 500643, China

    Kejia Chen

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  1. Linqin Cai
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  2. Nuoying Xu
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  3. Hang Tian
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Contributions

Linqin Cai to carry out the design of experimental ideas; Hang Tian conducted data collection and analysis; Haodu Fan performed the visualization of the experimental results; Nuoying Xu and Kejia Chen designed the software and wrote the first draft of the paper. All authors contributed to the review and revision of the first draft.

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Correspondence to Hang Tian.

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Cai, L., Xu, N., Tian, H. et al. Multimodal Bi-direction Guided Attention Networks for Visual Question Answering. Neural Process Lett 55, 11921–11943 (2023). https://doi.org/10.1007/s11063-023-11403-0

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