Vision Foundation Model

Vision foundation models (VFMs) are large-scale, pre-trained models designed to learn robust visual representations applicable across diverse downstream tasks, reducing the need for extensive task-specific training data. Current research emphasizes improving VFM efficiency and generalization through techniques like continual learning, semi-supervised fine-tuning, and knowledge distillation, often employing transformer-based architectures such as Vision Transformers (ViTs) and adapting them for specific applications like medical image analysis and autonomous driving. This work is significant because VFMs offer a more efficient and generalizable approach to computer vision, potentially accelerating progress in various fields by reducing the reliance on massive, task-specific datasets and enabling more robust and adaptable AI systems.