Cross Modal Understanding

Cross-modal understanding focuses on enabling machines to comprehend and integrate information from multiple sensory modalities, such as text, images, audio, and video, to achieve a more holistic understanding than is possible with unimodal processing. Current research emphasizes developing efficient model architectures, often leveraging contrastive learning and transformer-based approaches, to improve cross-modal alignment and representation learning, particularly within large language models. This field is crucial for advancing applications like video moment retrieval, visual question answering, and mental health detection, where integrating diverse data sources is essential for accurate and robust performance. Improved cross-modal understanding promises significant advancements in various fields by enabling more nuanced and comprehensive analysis of complex data.