Transferable Visual

Transferable visual representation learning aims to create visual models that generalize well across diverse datasets and tasks, minimizing the need for extensive retraining. Current research emphasizes developing methods to improve the transferability of pre-trained models, such as those based on diffusion models, CLIP, and vision-language models, often employing techniques like adapters, knowledge distillation, and prototype learning to adapt these models to new domains with limited data. This field is significant because it promises more efficient and robust computer vision systems, impacting applications ranging from image classification and object detection to medical imaging and autonomous driving.