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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
Universal Segmentation at Arbitrary Granularity with Language InstructionCode2
Unsupervised Universal Image SegmentationCode2
FOCUS: Towards Universal Foreground SegmentationCode2
One Model to Rule them All: Towards Universal Segmentation for Medical Images with Text PromptsCode2
SemiSAM+: Rethinking Semi-Supervised Medical Image Segmentation in the Era of Foundation ModelsCode2
The Missing Point in Vision Transformers for Universal Image SegmentationCode2
UniSeg: A Prompt-driven Universal Segmentation Model as well as A Strong Representation LearnerCode1
Segment Everything Everywhere All at OnceCode1
MedicoSAM: Towards foundation models for medical image segmentationCode1
SegAnyPET: Universal Promptable Segmentation from Positron Emission Tomography ImagesCode1
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