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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 110 of 32 papers

TitleStatusHype
The Missing Point in Vision Transformers for Universal Image SegmentationCode2
SemiSAM+: Rethinking Semi-Supervised Medical Image Segmentation in the Era of Foundation ModelsCode2
SegAnyPET: Universal Promptable Segmentation from Positron Emission Tomography ImagesCode1
MedicoSAM: Towards foundation models for medical image segmentationCode1
FOCUS: Towards Universal Foreground SegmentationCode2
VOILA: Complexity-Aware Universal Segmentation of CT images by Voxel Interacting with LanguageCode0
HyperSeg: Hybrid Segmentation Assistant with Fine-grained Visual PerceiverCode2
Towards Continual Universal Segmentation0
InstructSeg: Unifying Instructed Visual Segmentation with Multi-modal Large Language ModelsCode2
HyperSeg: Towards Universal Visual Segmentation with Large Language ModelCode2
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