The DOI matches its Crossref record; 40 of the 40 references it cites were found in a registry or an index.
Multi-modal Factorized Bilinear Pooling with Co-attention Learning for Visual Question Answering
Open accessAbstract
Visual question answering (VQA) is challenging because it requires a simultaneous understanding of both the visual content of images and the textual content of questions. The approaches used to represent the images and questions in a fine-grained manner and questions and to fuse these multimodal features play key roles in performance. Bilinear pooling based models have been shown to outperform traditional linear models for VQA, but their high-dimensional representations and high computational complexity may seriously limit their applicability in practice. For multimodal feature fusion, here we develop a Multi-modal Factorized Bilinear (MFB) pooling approach to efficiently and effectively combine multi-modal features, which results in superior performance for VQA compared with other bilinear pooling approaches. For fine-grained image and question representation, we develop a `co-attention' mechanism using an end-to-end deep network architecture to jointly learn both the image and question attentions. Combining the proposed MFB approach with co-attention learning in a new network architecture provides a unified model for VQA. Our experimental results demonstrate that the single MFB with co-attention model achieves new state-of-theart performance on the real-world VQA dataset. Code available at https://github.com/yuzcccc/mfb.
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Cites
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Research works printed but not linked: 1
- kim, 2016, Hadamard product for low-rank bilinear pooling
Cited by
Deep Modular Co-Attention Networks for Visual Question Answering
Yu, Z., Yu, J., Cui, Y., Tao, D., & Tian, Q. (2019) · 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2019.00644 · Cited by 964
Self-citationMultimodal intelligence: Representation learning, information fusion, and applications
Zhang, C., Yang, Z., He, X., & Deng, L. (2020) · IEEE Journal of Selected Topics in Signal Processing · doi:10.1109/jstsp.2020.2987728 · Cited by 511
Making the V in VQA Matter: Elevating the Role of Image Understanding in Visual Question Answering
Goyal, Y., Khot, T., Agrawal, A., Summers-Stay, D., Batra, D., & Parikh, D. (2019) · International Journal of Computer Vision · doi:10.1007/s11263-018-1116-0 · Cited by 487
Multiscale Feature Extraction and Fusion of Image and Text in VQA
Lu, S., Ding, Y., Liu, M., Yin, Z., Yin, L., & Zheng, W. (2023) · International Journal of Computational Intelligence Systems · doi:10.1007/s44196-023-00233-6 · Cited by 422
Multimodal Co-Attention Transformer for Survival Prediction in Gigapixel Whole Slide Images
Chen, R. J., Lu, M. Y., Weng, W.-H., Chen, T. Y., Williamson, D. F., Manz, T., Shady, M., & Mahmood, F. (2021) · 2021 IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv48922.2021.00398 · Cited by 398
Metadata
- Authors
- Yu, Z., Yu, J., Fan, J., & Tao, D.
- Venue
- 2017 IEEE International Conference on Computer Vision (ICCV)
- Publisher
- IEEE
- Published
- 2017-10
- Pages
- 1839-1848
- OpenAlex
- W2963150162
- Cited by
- 758
- References
- 40
- FWCI
- 19.58
- Type
- conference paper
- Language
- en
- Open access
- green
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