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Universal Segmentation

Universal segmentation is a challenging computer vision task that aims to segment images into semantic regions, regardless of the task or the domain. It requires the model to learn a wide range of visual concepts and to be able to generalize to new tasks and domains.

Papers

Showing 1120 of 32 papers

TitleStatusHype
SemiSAM+: Rethinking Semi-Supervised Medical Image Segmentation in the Era of Foundation ModelsCode2
MedUniSeg: 2D and 3D Medical Image Segmentation via a Prompt-driven Universal ModelCode2
The Missing Point in Vision Transformers for Universal Image SegmentationCode2
One Model to Rule them All: Towards Universal Segmentation for Medical Images with Text PromptsCode2
Unsupervised Universal Image SegmentationCode2
Universal Segmentation at Arbitrary Granularity with Language InstructionCode2
UniSeg: A Prompt-driven Universal Segmentation Model as well as A Strong Representation LearnerCode1
MedicoSAM: Towards foundation models for medical image segmentationCode1
CLUSTSEG: Clustering for Universal SegmentationCode1
SegAnyPET: Universal Promptable Segmentation from Positron Emission Tomography ImagesCode1
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