Visual in Context Learning

Visual in-context learning (VICL) aims to enable computer vision models to perform diverse tasks using only a few example images and associated textual descriptions, without requiring extensive retraining. Current research focuses on improving efficiency and accuracy through techniques like prompt selection algorithms, multimodal model architectures (e.g., incorporating transformers and vision-language models), and novel methods for fusing visual and textual information. This approach holds significant promise for reducing the need for large labeled datasets in computer vision, thereby accelerating progress in various applications, including image restoration, segmentation, and captioning.