Cross Modal Matching

Cross-modal matching focuses on aligning and comparing data from different modalities, such as images and text, to enable tasks like image retrieval using textual descriptions or semantic segmentation guided by captions. Current research emphasizes robust methods that handle noisy or incomplete data, often employing transformer-based architectures and contrastive learning techniques to improve the accuracy of cross-modal similarity measurement. These advancements are crucial for improving various applications, including scene understanding, information retrieval, and human-computer interaction, by enabling more effective integration of diverse data sources.