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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
MedUniSeg: 2D and 3D Medical Image Segmentation via a Prompt-driven Universal ModelCode2
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
Hierarchical Open-vocabulary Universal Image SegmentationCode2
Masked-attention Mask Transformer for Universal Image SegmentationCode2
SegAnyPET: Universal Promptable Segmentation from Positron Emission Tomography ImagesCode1
MedicoSAM: Towards foundation models for medical image segmentationCode1
CLUSTSEG: Clustering for Universal SegmentationCode1
Segment Everything Everywhere All at OnceCode1
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