Intra Modality

Intra-modality research focuses on understanding and leveraging the inherent relationships and structures within individual data modalities, such as text or images, in multi-modal learning tasks. Current research emphasizes developing methods that effectively capture intra-modal dependencies alongside inter-modal relationships, often employing advanced architectures like state space models and transformers, and incorporating regularization techniques to improve model robustness and efficiency. This work is crucial for enhancing the accuracy and efficiency of multi-modal applications across diverse fields, including medical image analysis, sentiment analysis, and vision-language retrieval, by ensuring that the internal structure of each modality is appropriately represented and utilized.