Study on Multimodal Retrieval Model Technology Based on Approximate Matching

With the rapid expansion of information and increasing complexity of user retrieval needs, traditional retrieval technologies can no longer fully meet practical application requirements. Research on general retrieval technologies based on large models has become urgent. This study focuses on this theme, aiming to achieve information retrieval using multimodal data integration and large language models. By analyzing the structure and characteristics of multimodal data, we explore efficient multimodal data representation and approximate matching techniques, and fine-tune the model for specific retrieval tasks. We propose the Approximate Matching for Multimodal Retrieval (AMMR) Model. The proposed model is comprehensively evaluated through experiments. The experimental results show that the AMMR model improves retrieval accuracy and robustness, better adapting to the evolving user needs.