An Image-Text Matching Method for Multi-Modal Robots

With the rapid development of artificial intelligence and deep learning, image-text matching has gradually become an important research topic in cross-modal fields. Achieving correct image-text matching requires a strong understanding of the correspondence between visual and textual information. In recent years, deep learning-based image-text matching methods have achieved significant success. However, image-text matching requires a deep understanding of intra-modal information and the exploration of fine-grained alignment between image regions and textual words. How to integrate these two aspects into a single model remains a challenge. Additionally, reducing the internal complexity of the model and effectively constructing and utilizing prior knowledge are also areas worth exploring, therefore addressing the issues of excessive computational complexity in existing fine-grained matching methods and the lack of multi-perspective matching.