Frozen Vision Language

Frozen vision-language models (VLMs), pre-trained on massive datasets and used without further training, are revolutionizing multimodal tasks. Current research focuses on leveraging these frozen models for diverse applications, including video segmentation, visual question answering, and open-vocabulary semantic segmentation, often employing techniques like in-context learning and prompt engineering to adapt them to specific downstream tasks. This approach offers significant advantages in efficiency and generalization, reducing the need for extensive task-specific training data and enabling zero-shot or few-shot performance on various visual and linguistic understanding problems. The resulting advancements have broad implications for computer vision, natural language processing, and robotics, promising more efficient and adaptable AI systems.