Vision Language Segmentation

Vision-language segmentation (VLS) aims to leverage the power of large vision-language models (VLMs) to perform image segmentation guided by textual descriptions. Current research focuses on improving the efficiency of adapting pre-trained VLMs to specific segmentation tasks, exploring techniques like prompt tuning and lightweight adapter modules to reduce computational costs and improve performance on limited data, including medical imaging. This field is significant because it promises more robust and adaptable segmentation models, particularly beneficial in domains with limited annotated data, leading to advancements in applications such as medical image analysis and autonomous driving.