Abstract: The groundbreaking segment anything model (SAM), built on a vision transformer (ViT) design with millions of parameters and trained on the large SA-1B dataset, acts as a vision foundation ...
Background: 3D medical image segmentation is a cornerstone for quantitative analysis and clinical decision-making in various modalities. However, acquiring high-quality voxel-level annotations is both ...
What if you could turn a simple photo into a fully realized 3D model, all without spending a dime? Below, Matthew Berman takes you through how SAM 3D, an open source platform from Meta, is ...
Background: This study aims to investigate the application of visual information processing mechanisms in the segmentation of stem cell (SC) images. The cognitive principles underlying visual ...
Less than four months after unveiling its video-focused Segment Anything Model 2, Meta has released SAM 3 and SAM 3D, immediately deploying the advanced computer vision models into consumer products ...
According to @AIatMeta, Meta has launched SAM 3, a unified AI model capable of object detection, segmentation, and tracking across both images and videos. SAM 3 introduces new features such as text ...
Meta Platforms Inc. today is expanding its suite of open-source Segment Anything computer vision models with the release of SAM 3 and SAM 3D, introducing enhanced object recognition and ...
We’re introducing SAM 3 and SAM 3D, the newest additions to our Segment Anything Collection, which advance AI understanding of the visual world. SAM 3 enables detection and tracking of objects in ...
The official PyTorch implementation for "SAM-Guided Prompt Learning for Multiple Sclerosis Lesion Segmentation". MS-SAMLess is a training-time distillation framework for Multiple Sclerosis lesion ...
Some results have been hidden because they may be inaccessible to you
Show inaccessible results