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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 2130 of 32 papers

TitleStatusHype
One Model to Rule them All: Towards Universal Segmentation for Medical Images with Text PromptsCode2
Universal Segmentation at Arbitrary Granularity with Language InstructionCode2
Hierarchical Open-vocabulary Universal Image SegmentationCode2
CLUSTSEG: Clustering for Universal SegmentationCode1
Segment Everything Everywhere All at OnceCode1
UniSeg: A Prompt-driven Universal Segmentation Model as well as A Strong Representation LearnerCode1
Towards Universal Vision-language Omni-supervised Segmentation0
OneFormer: One Transformer to Rule Universal Image SegmentationCode3
Training a universal instance segmentation network for live cell images of various cell types and imaging modalitiesCode0
Universal Segmentation of 33 Anatomies0
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