Unsupervised Instance Segmentation
8 papers with code • 2 benchmarks • 1 datasets
Benchmarks
These leaderboards are used to track progress in Unsupervised Instance Segmentation
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Libraries
Use these libraries to find Unsupervised Instance Segmentation models and implementations
UVO
Most implemented papers
Cut and Learn for Unsupervised Object Detection and Instance Segmentation
facebookresearch/cutler
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CVPR 2023
We propose Cut-and-LEaRn (CutLER), a simple approach for training unsupervised object detection and segmentation models.
Unsupervised Instance Segmentation in Microscopy Images via Panoptic Domain Adaptation and Task Re-weighting
More specifically, we first propose a nuclei inpainting mechanism to remove the auxiliary generated objects in the synthesized images.
PDAM: A Panoptic-Level Feature Alignment Framework for Unsupervised Domain Adaptive Instance Segmentation in Microscopy Images
dliu5812/PDAM
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In this work, we present an unsupervised domain adaptation (UDA) method, named Panoptic Domain Adaptive Mask R-CNN (PDAM), for unsupervised instance segmentation in microscopy images.
DARCNN: Domain Adaptive Region-based Convolutional Neural Network for Unsupervised Instance Segmentation in Biomedical Images
joyhsu0504/darcnn
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CVPR 2021
In the biomedical domain, there is an abundance of dense, complex data where objects of interest may be challenging to detect or constrained by limits of human knowledge.
DARCNN: Domain Adaptive Region-based Convolutional Neural Network forUnsupervised Instance Segmentation in Biomedical Images
joyhsu0504/darcnn
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CVPR 2021 2021
We showcase DARCNN’sperformance for unsupervised instance segmentation on nu-merous biomedical datasets.
DETReg: Unsupervised Pretraining with Region Priors for Object Detection
Recent self-supervised pretraining methods for object detection largely focus on pretraining the backbone of the object detector, neglecting key parts of detection architecture.
Unsupervised Universal Image Segmentation
u2seg/u2seg
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CVPR 2024
Several unsupervised image segmentation approaches have been proposed which eliminate the need for dense manually-annotated segmentation masks; current models separately handle either semantic segmentation (e. g., STEGO) or class-agnostic instance segmentation (e. g., CutLER), but not both (i. e., panoptic segmentation).
Part2Object: Hierarchical Unsupervised 3D Instance Segmentation
By training Hi-Mask3D on the objects and object parts extracted from Part2Object, we achieve consistent and superior performance compared to state-of-the-art models in various settings, including unsupervised instance segmentation, data-efficient fine-tuning, and cross-dataset generalization.
