Unsupervised Image Segmentation
16 papers with code • 2 benchmarks • 4 datasets
Oxford 102 Flower
COCO-Stuff
BN-HTRd
TYC Dataset
Most implemented papers
Unsupervised Deep Learning Meets Chan-Vese Model
zhengdharia/Deep_CV_segmentation
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The Chan-Vese (CV) model is a classic region-based method in image segmentation.
Self-Supervised Learning of Object Parts for Semantic Segmentation
mkuuwaujinga/leopart
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CVPR 2022
However, learning dense representations is challenging, as in the unsupervised context it is not clear how to guide the model to learn representations that correspond to various potential object categories.
Unsupervised Camouflaged Object Segmentation as Domain Adaptation
Jun-Pu/UCOS-DA
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To this end, we formulate the UCOS as a source-free unsupervised domain adaptation task (UCOS-DA), where both source labels and target labels are absent during the whole model training process.
Q-Seg: Quantum Annealing-based Unsupervised Image Segmentation
Thus, Q-Seg emerges as a viable alternative for real-world applications using available quantum hardware, particularly in scenarios where the lack of labeled data and computational runtime are critical.
Unsupervised Universal Image Segmentation
u2seg/u2seg
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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).
UnSegGNet: Unsupervised Image Segmentation using Graph Neural Networks
Image segmentation, the process of partitioning an image into meaningful regions, plays a pivotal role in computer vision and medical imaging applications.
