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Unsupervised Panoptic Segmentation datasets

archive 2025-07-28

5 datasets carry the task tag "Unsupervised Panoptic Segmentation" (the task itself: Unsupervised Panoptic Segmentation), ordered by the archive's paper count. Page 1 of 1: 5 shown of 5. Facet routes are this site's own (the archive records the tag string, not a page).

The archive holds 12,214 dataset rows; 12,172 are listed. 6 are withheld from every listing and count here as vandalised before snapshot (6 with contact-centre spam in the title, 0 with a spam description on a row that has no homepage, no paper and no papers counted; none with more than 1 paper, 0 with a benchmark), listed in withheld.json; 1 listed row carries a vandalised description, withheld on its page. This gate never withholds a row with a homepage or a paper that resolves, and a clean description; the content rules below withhold a row whose name is spam whatever else it carries. The gate is a phrase list: these are the rows it caught, not a claim that the rest is clean. Before that gate, the site's content rules withhold 36 more rows (invite-code, gambling, travel-booking, contact-centre and similar spam in the name or on a row with nothing real behind it); they have no page and are listed in withheld.json.

Filter 51 task tags shown of 3,717, by dataset count; the full filter by modality, task and language is on /datasets

All 12,172 Question Answering 413Semantic Segmentation 347Object Detection 332Image Classification 283Classification 222Text Classification 167Language Modelling 166Text Generation 162Visual Question Answering (VQA) 144Named Entity Recognition (NER) 130Pose Estimation 124Anomaly Detection 119Action Recognition 115Instance Segmentation 111Sentiment Analysis 104Text Summarization 98Domain Adaptation 96Speech Recognition 96Reading Comprehension 95Information Retrieval 932D Object Detection 92Image Retrieval 87Machine Translation 82Relation Extraction 822D Semantic Segmentation 81Natural Language Inference 81Image Captioning 79Image Generation 79Depth Estimation 77Node Classification 75Natural Language Understanding 72Autonomous Driving 70Code Generation 70Object Tracking 693D Object Detection 67Face Recognition 67Link Prediction 67Person Re-Identification 65Data Augmentation 63Multi-Task Learning 593D Reconstruction 58Common Sense Reasoning 56Recommendation Systems 56Video Understanding 56Optical Character Recognition (OCR) 55Graph Classification 54Abstractive Text Summarization 52Emotion Recognition 52Medical Image Segmentation 52Word Embeddings 52Unsupervised Panoptic Segmentation 5

Unsupervised Panoptic Segmentation datasets 1–5 of 5

Cityscapes
Cityscapes is a large-scale database which focuses on semantic understanding of urban street scenes.
ImagesSemantic SegmentationImage Generation2D Semantic SegmentationInstance SegmentationScene ParsingImage-to-Image Translation+27 tasks
3,702 papers · 51 benchmarks
KITTI
KITTI (Karlsruhe Institute of Technology and Toyota Technological Institute) is one of the most popular datasets for use in mobile robotics and autonomous driving.
ImagesLiDARObject DetectionSemantic SegmentationPose EstimationObject Tracking3D Object DetectionImage Super-Resolution+41 tasks
3,661 papers · 137 benchmarks
Waymo Open Dataset
The Waymo Open Dataset is comprised of high resolution sensor data collected by autonomous vehicles operated by the Waymo Driver in a wide variety of conditions.
Images3DLiDARObject Detection3D Object Detection3D Human Pose Estimation3D Semantic SegmentationMultiple Object TrackingAutonomous Driving+4 tasks
481 papers · 16 benchmarks
BDD100K
Datasets drive vision progress, yet existing driving datasets are impoverished in terms of visual content and supported tasks to study multitask learning for autonomous driving.
VideosImage Classification2D Object DetectionObject DetectionSemantic SegmentationDomain AdaptationInstance Segmentation+14 tasks
469 papers · 16 benchmarks
MUSES: MUlti-SEnsor Semantic perception dataset (The Multi-Sensor Semantic Perception Dataset for Driving under Uncertainty)
MUSES offers 2500 multi-modal scenes, evenly distributed across various combinations of weather conditions (clear, fog, rain, and snow) and types of illumination (daytime, nighttime).
ImagesPoint cloudRGB-DLiDARObject DetectionSemantic SegmentationPanoptic SegmentationUnsupervised Panoptic SegmentationUncertainty-Aware Panoptic Segmentation
4 papers · 5 benchmarks

Paper counts and descriptions are the archive's, frozen 2025-07-28; no citation counts, no stars, no trending. Sorting by "most cited" or "newest" was a live-site feature the archive does not carry.