Unsupervised Panoptic Segmentation
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Benchmarks
Most implemented
Unsupervised Universal Image Segmentation
Cut and Learn for Unsupervised Object Detection and Instance Segmentation
Scene-Centric Unsupervised Panoptic Segmentation
Papers
Scene-Centric Unsupervised Panoptic Segmentation
Unsupervised panoptic segmentation aims to partition an image into semantically meaningful regions and distinct object instances without training on manually annotated data. In contrast to prior work on unsupervised pano…
Instance SegmentationPanoptic SegmentationPseudo LabelScene Understanding+5Unsupervised Universal Image Segmentation
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) …
Image SegmentationInstance SegmentationPanoptic SegmentationSegmentation+8Unsupervised Semantic Segmentation Through Depth-Guided Feature Correlation and Sampling
Traditionally, training neural networks to perform semantic segmentation required expensive human-made annotations. But more recently, advances in the field of unsupervised learning have made significant progress on this…
Feature CorrelationSemantic SegmentationUnsupervised Panoptic SegmentationUnsupervised Semantic SegmentationCut and Learn for Unsupervised Object Detection and Instance Segmentation
We propose Cut-and-LEaRn (CutLER), a simple approach for training unsupervised object detection and segmentation models. We leverage the property of self-supervised models to 'discover' objects without supervision and am…
Instance Segmentationobject-detectionObject DetectionSemantic Segmentation+4