Abstract We introduce the Segment Anything (SA) project: a new task, model, and dataset for
image segmentation. Using our efficient model in a data collection loop, we built the largest …

We present Segment Anything Model 2 (SAM 2), a foundation model towards solving
promptable visual segmentation in images and videos. We build a data engine, which …

We present the evaluation methodology, datasets and results of the BOP Challenge 2022,
the fourth in a series of public competitions organized with the goal to capture the status quo …

Abstract 6D object pose estimation networks are limited in their capability to scale to large
numbers of object instances due to the close-set assumption and their reliance on high …

Segment anything model (SAM) is a promising prompt-guided vision foundation model to
segment objects of interest. However, the extensive computational requirements of SAM …

Abstract The Segment Anything Model (SAM) is a large-scale foundation model that has
revolutionized segmentation methodology. Despite its impressive generalization ability, the …

Currently, multimedia systems and computer vision algorithms are increasingly playing a
crucial role in biological research. However, due to the significant difference between macro …

Segment Anything Model (SAM) has shown impressive performance in interactive and zero-
shot segmentation across diverse domains, suggesting that they have learned a general …

The recent segmentation foundation model, Segment Anything Model (SAM), exhibits strong
zero-shot segmentation capabilities, but it falls short in generating fine-grained precise …

In medical imaging, precise annotation of lesions or organs is often required. However, 3D
volumetric images typically consist of hundreds or thousands of slices, making the …