Input Dependent Prompt

Input-dependent prompting focuses on dynamically tailoring prompts for large vision-language models (VLMs) to improve performance on diverse downstream tasks, particularly in open-set and few-shot learning scenarios. Current research emphasizes developing methods to generate these prompts, including approaches based on test-time tuning, composable prompts, and dual-context learning, often leveraging the capabilities of LLMs to enhance prompt design. This research is significant because it allows for efficient adaptation of powerful, pre-trained models to new tasks and domains without extensive retraining, leading to more flexible and robust AI systems across various applications.