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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 11–20 of 32 papers

TitleStatusHype
Large Language Model with Region-guided Referring and Grounding for CT Report GenerationCode2
Rethinking the Evaluation of Visible and Infrared Image FusionCode3
MedUniSeg: 2D and 3D Medical Image Segmentation via a Prompt-driven Universal ModelCode2
OMG-LLaVA: Bridging Image-level, Object-level, Pixel-level Reasoning and UnderstandingCode5
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
Unsupervised Universal Image SegmentationCode2
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