Vision Foundation Model Adaptation

This field studies how to reuse pretrained vision models when target tasks, data, or sensors differ from their original training conditions. Common approaches include full or partial fine-tuning, parameter-efficient methods such as LoRA and prompt or adapter tuning, feature alignment, and distillation into smaller models. Applications range from medical and remote-sensing segmentation to video understanding, robotics, and 3D geometry, often with limited labels or shifted test data. A central tension is that adaptation can improve target-task accuracy while weakening robustness or transfer to unfamiliar settings, so efficiency and out-of-domain performance remain important evaluation criteria.