Cross Modality Transfer

Cross-modality transfer focuses on leveraging knowledge learned from one data modality (e.g., images) to improve performance on tasks involving a different modality (e.g., text or sensor data). Current research emphasizes developing efficient methods for transferring information between modalities, often employing techniques like adapter modules, contrastive learning, and diffusion models within various architectures including foundation models and transformers. This field is crucial for advancing AI capabilities in areas like medical image analysis, robotics, and human activity recognition, where integrating information from multiple sensors is essential for robust and accurate performance. The development of parameter-efficient transfer learning methods is a key focus, aiming to reduce computational costs and improve generalization across diverse modalities.