Modal Similarity

Modal similarity research focuses on developing methods to effectively compare and integrate information from different data modalities (e.g., text and images, audio and video). Current research emphasizes improving cross-modal alignment through techniques like contrastive learning and attention mechanisms, often leveraging pre-trained models such as CLIP, and exploring multi-scale and fine-grained similarity measures to capture nuanced relationships. This work is crucial for advancing applications in diverse fields, including image captioning, semantic location prediction, and multimodal retrieval, by enabling more accurate and robust information fusion across various data types.