MSAM: Deep Semantic Interaction Network for Visual Question Answering
Author:
Publisher
Springer Nature Switzerland
Link
https://link.springer.com/content/pdf/10.1007/978-3-031-54528-3_3
Reference57 articles.
1. Gao, P., Jiang, Z., You, H., et al.: Dynamic fusion with intra-and inter-modality attention flow for visual question answering. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 6639–6648 (2019)
2. Zhang, W., Yu, J., Hu, H., et al.: Multimodal feature fusion by relational reasoning and attention for visual question answering. Inf. Fusion 55, 116–126 (2020)
3. Chen, T., Yu, W., Chen, R., et al.: Knowledge-embedded routing network for scene graph generation. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 6163–6171 (2019)
4. Zhou, H., Du, J., Zhang, Y., et al.: Information fusion in attention networks using adaptive and multi-level factorized bilinear pooling for audio-visual emotion recognition. IEEE/ACM Trans. Audio Speech Lang. Process. 29, 2617–2629 (2021)
5. Tan, H., Bansal, M.: LXMERT: learning cross-modality encoder representations from transformers. arXiv preprint arXiv:1908.07490 (2019)
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