Cross Modality Matching

Cross-modality matching focuses on aligning and comparing data from different sensory modalities (e.g., images and text, visible and infrared light, images and point clouds). Current research emphasizes developing robust algorithms, often leveraging contrastive learning, optimal transport, and pre-trained models like CLIP, to bridge the "modality gap" and improve cross-modal matching accuracy. This work is crucial for applications ranging from person re-identification and medical image analysis to zero-shot learning and image retrieval, enabling more powerful and versatile AI systems. Significant advancements are being made through techniques like generating homogeneous modalities and incorporating multi-granularity feature extraction.