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

Due to the inherent flexibility of prompting, foundation models have emerged as the
predominant force in the fields of natural language processing and computer vision. The …

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 landscape of publicly available vision foundation models (VFMs) such as CLIP and
SAM is expanding rapidly. VFMs are endowed with distinct capabilities stemming from their …

This paper introduces SAMAug, a novel visual point augmentation method for the Segment
Anything Model (SAM) that enhances interactive image segmentation performance …

The long-tailed distribution problem in medical image analysis reflects a high prevalence of
common conditions and a low prevalence of rare ones which poses a significant challenge …

Despite that the segment anything model (SAM) achieved impressive results on general-
purpose semantic segmentation with strong generalization ability on daily images, its …

In this paper, we present a large-scale evaluation probing GPT-4V's capabilities and
limitations for biomedical image analysis. GPT-4V represents a breakthrough in artificial …

Image segmentation achieves significant improvements with deep neural networks at the
premise of a large scale of labeled training data, which is laborious to assure in medical …

Segment Anything Model (SAM) has achieved impressive results for natural image
segmentation with input prompts such as points and bounding boxes. Its success largely …