Image Recognition Method Based on the Fusion of Multimodal Large Models

With the development of deep learning technology, image recognition has made significant progress in various fields. However, the performance of recognition models is limited by single-modal information, especially in complex scenarios where traditional deep learning methods lack generalization ability. This paper proposes an image recognition method based on multimodal large model fusion (BFMLM), which leverages the correlation between multimodal data to achieve cross-modal feature fusion, improving the accuracy and robustness of the model in target recognition. Experiments show that, compared to traditional deep learning-based single-modal image recognition algorithms, the proposed method performs better on multiple datasets.