Visual prompt engineering is a fundamental methodology in the field of visual and image
artificial general intelligence. As the development of large vision models progresses, the …

This article discusses the opportunities, applications and future directions of large-scale
pretrained models, ie, foundation models, which promise to significantly improve the …

Medical image segmentation is a critical component in clinical practice, facilitating accurate
diagnosis, treatment planning, and disease monitoring. However, existing methods, often …

Training segmentation models for medical images continues to be challenging due to the
limited availability of data annotations. Segment Anything Model (SAM) is a foundation …

Abstract The Segment Anything Model (SAM) is the first foundation model for general image
segmentation. It has achieved impressive results on various natural image segmentation …

The Segment Anything Model (SAM) has recently gained popularity in the field of image
segmentation. Thanks to its impressive capabilities in all-round segmentation tasks and its …

The scarcity of data presents a critical obstacle to the efficacy of medical vision-language pre-
training (VLP). A potential solution lies in the combination of datasets from various language …

The Segment Anything Model (SAM) made an eye-catching debut recently and inspired
many researchers to explore its potential and limitation in terms of zero-shot generalization …

Automatic segmentation of colon polyps can significantly reduce the misdiagnosis of colon
cancer and improve physician annotation efficiency. While many methods have been …

The Segment Anything Model (SAM) is a powerful foundation model that has revolutionised
image segmentation. To apply SAM to surgical instrument segmentation, a common …