Abstract: Referring Image Segmentation, the task of finding and segmenting objects in an image conditioned on a natural language description, is crucial for human-robot collaboration. However, current ...

Introduction: The combination of CNN and Transformer has attracted much attention for medical image segmentation due to its superior performance at present. However, the segmentation performance is ...

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 ...

Annotating regions of interest in medical images, a process known as segmentation, is often one of the first steps clinical researchers take when running a new study involving biomedical images. For ...

This repository is the official implementation of the paper RSRefSeg 2: Decoupling Referring Remote Sensing Image Segmentation with Foundation Models, developed based on the OpenMMLab codebase. The ...

Royalty-free licenses let you pay once to use copyrighted images and video clips in personal and commercial projects on an ongoing basis without requiring additional payments each time you use that ...

As shown below, the inferred masks predicted by our segmentation model trained on the PNG dataset appear similar to the ground truth masks, but lack precision in some areas. To improve segmentation ...

1 School of Biomedical Engineering, Sichuan University, Chengdu, China 2 National Clinical Research Center for Oral Diseases, West China Hospital of Stomatology, Sichuan University, Chengdu, China ...

What if you could predict the future—not just in abstract terms, but with actionable precision? From forecasting energy demand to anticipating retail trends, the ability to make accurate predictions ...

Abstract: This study presents an innovative segmentation algorithm that seamlessly integrates an adaptive multi-scale attention mechanism with the Lossab loss function to tackle challenges like ...