Cross-Attention Transformer-Based Visual-Language Fusion for Multimodal Image Analysis
Keywords:
multimodal image analysis, Cross-Attention Transformer, visual-language fusion, cross-attention mechanism
Abstract
Multimodal image analysis is a significant research direction in the field of computer vision, playing a crucial role in tasks such as image captioning and visual question answering (VQA). However, existing visual-language fusion methods often struggle to capture the fine-grained interactions between visual and language modalities, leading to suboptimal fusion results. To address this issue, this paper proposes a visual-language fusion model based on the Cross-Attention Transformer, which constructs deep interactive relationships between visual and language modalities through cross-attention mechanisms, thereby achieving effective multimodal feature fusion. The proposed model first utilizes convolutional neural networks (CNN) and pre-trained language models (e.g., BERT) to extract visual and language features separately, and then applies cross-attention modules to capture mutual dependencies in feature sequences, resulting in a unified multimodal representation vector. Experimental results demonstrate that the proposed model significantly outperforms traditional methods in tasks such as image captioning and VQA, validating its superiority in multimodal image analysis. Additionally, visualization analysis and ablation experiments further explore the contribution of the cross-attention mechanism to model performance, while discussing the model's limitations and potential future improvements.
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[30] Wang, Z., et al. (2024). Improved Unet model for brain tumor image segmentation based on ASPP-coordinate attention mechanism. In 2024 5th International Conference on Big Data & Artificial Intelligence & Software Engineering (ICBASE). IEEE.
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[35] Li, Z., Wang, B., & Chen, Y. (2024). Knowledge graph embedding and few-shot relational learning methods for digital assets in USA. Journal of Industrial Engineering and Applied Science, 2(5), 10-18.
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[40] Li, Z., Wang, B., & Chen, Y. (2024). A contrastive deep learning approach to cryptocurrency portfolio with US treasuries. Journal of Computer Technology and Applied Mathematics, 1(3), 1-10.
[41] Weng, Y., Wu, J., Kelly, T., et al. (2024). Comprehensive overview of artificial intelligence applications in modern industries. arXiv preprint arXiv:2409.13059.
[42] Huang, B., Lu, Q., Huang, S., et al. (2024). Multi-modal clothing recommendation model based on large model and VAE enhancement. arXiv preprint arXiv:2410.02219.
[43] Li, Z., Wang, B., & Chen, Y. (2024). Knowledge graph embedding and few-shot relational
learning methods for digital assets in USA. Journal of Industrial Engineering and Applied Science, 2(5), 10-18.
[44] Zhao, P., & Lai, L. (2024). Minimax optimal q learning with nearest neighbors. IEEE Transactions on Information Theory.
[45] Feng, J., Wu, Y., Sun, H., Zhang, S., & Liu, D. (2025). Panther: Practical secure 2-party neural network inference. IEEE Transactions on Information Forensics and Security.
Published
2025-02-25
How to Cite
Ding, L., Shih, K., Wen, H., Li, X., & Yang, Q. (2025, February 25). Cross-Attention Transformer-Based Visual-Language Fusion for Multimodal Image Analysis. International Journal of Applied Science, 8(1), p27. https://doi.org/https://doi.org/10.30560/ijas.v8n1p27
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