Research on Cross-Modal Recognition Method Based on KDCA-CLIP

Based on the limitations of the CLIP model in cross-modal learning, this study proposes an enhanced model that combines the cross-attention mechanism and knowledge distillation technology. The cross-attention mechanism can effectively capture the complex relationships between images and text, enhancing the model's expressive ability. Knowledge distillation can extract knowledge from a well-trained teacher model to further improve the performance of the student model. Experiments were conducted on the Flickr30k dataset and the MSCOCO2017 dataset. The experimental results show that the model significantly improves the accuracy and robustness in the image-text matching task. In the image-text retrieval task, compared with the traditional CLIP model, the performance is enhanced, thereby verifying the effectiveness of the cross-attention and knowledge distillation methods.