Shot Vision Language

Shot vision-language research focuses on adapting large pre-trained vision-language models (like CLIP) to new tasks with limited labeled data. Current efforts concentrate on improving few-shot learning techniques, exploring methods such as prompt engineering, parameter-efficient fine-tuning (e.g., LoRA), and optimized task sampling strategies to enhance model generalization and reduce computational costs. These advancements aim to bridge the gap between impressive zero-shot capabilities and the need for robust few-shot performance in diverse vision-language applications, ultimately leading to more efficient and effective AI systems.