Cross-Attention Transformer-Based Visual-Language Fusion for Multimodal Image Analysis

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.