Cross-modal Retrieval Based on Multi-modal Large Model With Convolutional Attention and Adversarial Training

With the continuous development of large cross-modal models, this approach to addressing cross-modal retrieval challenges has become mainstream in the field of information retrieval. However, two major issues still persist in cross-modal retrieval: the intermodal semantic gap and the generalization of cross-modal models. The first issue is particularly challenging. Although multi-modal large models can help obtain unified semantic representations of different modalities in a shared embedding space, they still struggle to fully capture image representations, which limits their ability to understand natural language.