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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

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Showing 21–30 of 32 papers

TitleStatusHype
UniSeg: A Prompt-driven Universal Segmentation Model as well as A Strong Representation LearnerCode1
VOILA: Complexity-Aware Universal Segmentation of CT images by Voxel Interacting with LanguageCode0
Towards Continual Universal Segmentation—0
Beyond Human Vision: The Role of Large Vision Language Models in Microscope Image Analysis—0
RadGenome-Chest CT: A Grounded Vision-Language Dataset for Chest CT AnalysisCode0
Progressive Token Length Scaling in Transformer Encoders for Efficient Universal SegmentationCode0
COCONut: Modernizing COCO Segmentation—0
SegICL: A Multimodal In-context Learning Framework for Enhanced Segmentation in Medical Imaging—0
Towards Universal Vision-language Omni-supervised Segmentation—0
Training a universal instance segmentation network for live cell images of various cell types and imaging modalitiesCode0
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