The Segment Anything Model (SAM) has recently gained popularity in the field of image
segmentation due to its impressive capabilities in various segmentation tasks and its prompt …

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 …

We propose SAMed, a general solution for medical image segmentation. Different from the
previous methods, SAMed is built upon the large-scale image segmentation model …

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

The Segment Anything Model (SAM) has rapidly been adopted for segmenting a wide range
of natural images. However, recent studies have indicated that SAM exhibits subpar …

The recently introduced Segment Anything Model (SAM) combines a clever architecture and
large quantities of training data to obtain remarkable image segmentation capabilities …

Abstract The Segment Anything Model (SAM) is a recently developed large model for
general-purpose segmentation for computer vision tasks. SAM was trained using 11 million …

The Segment Anything Model (SAM) represents a state-of-the-art research advancement in
natural image segmentation, achieving impressive results with input prompts such as points …

Deep learning based automatic medical image segmentation models often suffer from
domain shift, where the models trained on a source domain do not generalize well to other …

The Segment Anything Model (SAM), a foundation model for general image segmentation,
has demonstrated impressive zero-shot performance across numerous natural image …