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Resources of semantic segmantation based on Deep Learning model
| Date | Stars |
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| 2026-07-31 | 1102 |
| 2026-08-06 | 1102 |
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# Semantic-Segmentation A list of all papers and resoureces on Semantic Segmentation. # Dataset importance  # SemanticSegmentation_DL Some implementation of semantic segmantation for DL model</br> ## Dataset + [voc2012](http://host.robots.ox.ac.uk/pascal/VOC/voc2012/) + [CitySpaces](https://www.cityscapes-dataset.com/) + [Mapillary](https://www.mapillary.com/dataset/vistas) + [ADE20K](http://groups.csail.mit.edu/vision/datasets/ADE20K/) + [PASCAL Context](http://www.cs.stanford.edu/~roozbeh/pascal-context/) + [COCO-Stuff 10K dataset v1.1](https://github.com/nightrome/cocostuff) + [2D-3D-S dataset](http://buildingparser.stanford.edu/dataset.html) + [Mapillary Vistas](https://www.youtube.com/karolmajek) + [Stanford Background Dataset](http://dags.stanford.edu/projects/scenedataset.html) + [Sift Flow Dataset](http://people.csail.mit.edu/celiu/SIFTflow/) + [Barcelona Dataset](http://www.cs.unc.edu/~jtighe/Papers/ECCV10/) + [Microsoft COCO dataset](http://mscoco.org/) + [MSRC Dataset](http://research.microsoft.com/en-us/projects/objectclassrecognition/) + [LITS Liver Tumor Segmentation Dataset](https://competitions.codalab.org/competitions/15595) + [KITTI](http://www.cvlibs.net/datasets/kitti/eval_road.php) + [Pascal Context](http://www.cs.stanford.edu/~roozbeh/pascal-context/) + [Data from Games dataset](https://download.visinf.tu-darmstadt.de/data/from_games/) + [Human parsing dataset](https://github.com/lemondan/HumanParsing-Dataset) + [Mapillary Vistas Dataset](https://www.mapillary.com/dataset/vistas) + [Microsoft AirSim](https://github.com/Microsoft/AirSim) + [MIT Scene Parsing Benchmark](http://sceneparsing.csail.mit.edu/) + [COCO 2017 Stuff Segmentation Challenge](http://cocodataset.org/#stuff-challenge2017) + [ADE20K Dataset](http://groups.csail.mit.edu/vision/datasets/ADE20K/) + [INRIA Annotations for Graz-02](http://lear.inrialpes.fr/people/marszalek/data/ig02/) + [Daimler dataset](http://www.gavrila.net/Datasets/Daimler_Pedestrian_Benchmark_D/daimler_pedestrian_benchmark_d.html) + [ISBI Challenge: Segmentation of neuronal structures in EM stacks](http://brainiac2.mit.edu/isbi_challenge/) + [INRIA Annotations for Graz-02 (IG02)](https://lear.inrialpes.fr/people/marszalek/data/ig02/) + [Pratheepan Dataset](http://cs-chan.com/downloads_skin_dataset.html) + [Clothing Co-Parsing (CCP) Dataset](https://github.com/bearpaw/clothing-co-parsing) ## Resources ## Survey papers - A 2017 Guide to Semantic Segmentation with Deep Learning by Qure AI [[Blog about different sem. segm. methods]](http://blog.qure.ai/notes/semantic-segmentation-deep-learning-review) - A Review on Deep Learning Techniques Applied to Semantic Segmentation [[Survey paper with a special focus on datasets and the highest performing methods]](https://arxiv.org/abs/1704.06857) - Computer Vision for Autonomous Vehicles: Problems, Datasets and State-of-the-Art [[Survey paper about all aspects of autonomous vehicles, including sem. segm.]](https://arxiv.org/abs/1704.05519) [[Webpage with a summary of all relevant publications]](http://www.cvlibs.net/projects/autonomous_vision_survey/) - A Survey on Deep Learning in Medical Image Analysis [[Paper]](https://arxiv.org/pdf/1702.05747） ## Online demos - [CRF as RNN](http://www.robots.ox.ac.uk/~szheng/crfasrnndemo) - [SegNet](http://mi.eng.cam.ac.uk/projects/segnet/demo.php#demo) ## 2D Semantic Segmentation ### Papers: - [2019-CVPR oral] CLAN: Category-level Adversaries for Semantics Consistent [[`paper`]](https://arxiv.org/abs/1809.09478?context=cs) [[`code`]](https://github.com/RoyalVane/CLAN) - [2019-CVPR] BRS: Interactive Image Segmentation via Backpropagating Refinement Scheme(***) [[`paper`]](https://vcg.seas.harvard.edu/publications/interactive-image-segmentation-via-backpropagating-refinement-scheme/paper) [[`code`]](https://github.com/wdjang/BRS-Interactive_segmentation) - [2019-CVPR] DFANe
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Phil Ferriere · Freelance · United States
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Would you bet a product on this? Bounded 0–100 and slow moving.
matched fp:f06d22c8cc9fc38f, llm:description: 'Resources of semantic segmantation based on Deep Learning model' (semantic segmentation DL resources)
matched fp:f06d22c8cc9fc38f, llm:description: 'Resources of semantic segmantation based on Deep Learning model' (semantic segmentation DL resources)
matched fp:f06d22c8cc9fc38f, llm:description: 'Resources of semantic segmantation based on Deep Learning model' (semantic segmentation DL resources)