Modality Adaptation

Modality adaptation focuses on effectively integrating information from different data types (e.g., images, audio, text) within machine learning models, aiming to improve performance and robustness. Current research emphasizes efficient methods, such as lightweight modules and parameter-efficient fine-tuning techniques like LoRA, to adapt pre-trained models for multimodal tasks, often addressing challenges like missing modalities or the "modality gap" between different input types. This work is crucial for advancing multimodal AI, impacting diverse applications ranging from disease diagnosis and brain tumor segmentation to improved large language models and more robust speech-to-text translation.