{"id":18527898,"url":"https://github.com/nemonameless/segmentation-paper-list","last_synced_at":"2026-01-24T21:13:08.154Z","repository":{"id":118413218,"uuid":"174387340","full_name":"nemonameless/segmentation-paper-list","owner":"nemonameless","description":"Collection of online resources about segmentation.","archived":false,"fork":false,"pushed_at":"2020-06-15T09:47:05.000Z","size":14,"stargazers_count":8,"open_issues_count":0,"forks_count":0,"subscribers_count":3,"default_branch":"master","last_synced_at":"2024-12-26T01:42:13.370Z","etag":null,"topics":[],"latest_commit_sha":null,"homepage":"","language":null,"has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":null,"status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/nemonameless.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":null,"code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null,"governance":null,"roadmap":null,"authors":null,"dei":null,"publiccode":null,"codemeta":null}},"created_at":"2019-03-07T17:07:04.000Z","updated_at":"2024-12-02T08:47:51.000Z","dependencies_parsed_at":null,"dependency_job_id":"33c0fb20-cda3-4a93-ad16-07f18c16891f","html_url":"https://github.com/nemonameless/segmentation-paper-list","commit_stats":null,"previous_names":[],"tags_count":0,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/nemonameless%2Fsegmentation-paper-list","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/nemonameless%2Fsegmentation-paper-list/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/nemonameless%2Fsegmentation-paper-list/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/nemonameless%2Fsegmentation-paper-list/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/nemonameless","download_url":"https://codeload.github.com/nemonameless/segmentation-paper-list/tar.gz/refs/heads/master","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":239243974,"owners_count":19606346,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2022-07-04T15:15:14.044Z","host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":[],"created_at":"2024-11-06T17:56:15.864Z","updated_at":"2025-11-01T01:30:25.059Z","avatar_url":"https://github.com/nemonameless.png","language":null,"funding_links":[],"categories":[],"sub_categories":[],"readme":"# segmentation-paper-list\n# Segmentation\nCollection of online resources about segmentation.\n\nFormat: [简称 - 全名 - 刊名 - 时间](paper超链接)[[code](code超连接)]\n\n## Datasets\n\n1. Panoramic Segmentation\n     - [Cityscapes](https://www.cityscapes-dataset.com/)\n     - [ADE20k](http://groups.csail.mit.edu/vision/datasets/ADE20K/)\n     - [Mapillary Vistas](https://blog.mapillary.com/product/2017/05/03/mapillary-vistas-dataset.html)\n     - [COCO](http://cocodataset.org)\n\n## Challenges\n\n- [COCO](http://cocodataset.org/#home)\n- [ECCV2018 COCO + Mapillary](http://cocodataset.org/workshop/coco-mapillary-eccv-2018.html)\n\n## Evaluation Metric\n\n1. Semantic segmentation\n   - mIoU\n   - PA\n   - MPA\n   - FWIoU\n   - speed\n   - net size\n\n2. [Panoptic segmentation](https://arxiv.org/abs/1801.00868)\n   - segmentation quality(SQ)\n   - detection quality(DQ)\n   - PQ\n \n3. Backgrond/Foreground Segmentation\n   -  mean absolute error(MAE)\n   -  F-measure\n\n\n\n## Demos \u0026\u0026 Appllications\n\n## Opensource Projects\n\n- [MMDetection](https://github.com/open-mmlab/mmdetection)\n\n## Online Resources\n\n- [Paper list - awesome-panoptic-segmentation](https://github.com/Angzz/awesome-panoptic-segmentation)\n- [Paper list - awesome-semantic-segmentation](https://github.com/mrgloom/awesome-semantic-segmentation)\n- [Paper list - Semantic-Segmentation_DL](https://github.com/tangzhenyu/SemanticSegmentation_DL)\n\n## Papers \u0026 Documents\n\n### Classical Segmentation\n\n1. Graph Cut\n   - Min-Cut / Max-Flow\n   - [Graph Cuts - Interactive Graph Cuts for Optimal Boundary \u0026 Region Segmentation of Objects in N-D Images - ICCV - 2001](http://www.csd.uwo.ca/~yuri/Papers/iccv01.pdf)\n   - [GrabCut - Interactive Foreground Extraction using Iterated Graph Cuts - SIGGRAPH - 2004](https://cvg.ethz.ch/teaching/cvl/2012/grabcut-siggraph04.pdf)\n   - [NCut - Normalized Cuts and Image Segmentation - PAMI -2000](https://people.eecs.berkeley.edu/~malik/papers/SM-ncut.pdf)\n2. Contour Model\n   - SNAKE\n   - Level Set\n3. Superpixel\n   - [SLIC - SLIC Superpixels - EPFL Technical Report - 2010](http://www.kev-smith.com/papers/SLIC_Superpixels.pdf)\n   - [LSC - Superpixel Segmentation using Linear Spectral Clustering - CVPR - 2015](https://www.cv-foundation.org/openaccess/content_cvpr_2015/papers/Li_Superpixel_Segmentation_Using_2015_CVPR_paper.pdf)\n\n### Backgrond/Foreground Segmentation\n   - [Saliency Optimization from Robust Detection - CVPR - 2014](https://www.cv-foundation.org/openaccess/content_cvpr_2014/papers/Zhu_Saliency_Optimization_from_2014_CVPR_paper.pdf)\n   - [Deeply supervised salient object detection with short connections - CVPR - 2017] (http://openaccess.thecvf.com/content_cvpr_2017/papers/Hou_Deeply_Supervised_Salient_CVPR_2017_paper.pdf) [[code-pytorch](https://github.com/AceCoooool/DSS-pytorch)][[code-caffe](https://github.com/Andrew-Qibin/DSS)]\n\n## Semantic/Stuff Segmentation\n### Effective\n\n- [Large Kernel Matters -- Improve Semantic Segmentation by Global Convolutional Network - CVPR - 2017](https://arxiv.org/abs/1703.02719)\n- [Learning a Discriminative Feature Network for Semantic Segmentation - CVPR - 2018](https://arxiv.org/abs/1804.09337)\n- [Pyramid Attention Network for Semantic Segmentation - ARXIV - 2018](https://arxiv.org/abs/1805.10180v3)\n- [Context Encoding for Semantic Segmentation - CVPR - 2018](https://arxiv.org/abs/1803.08904) [[code](https://github.com/zhanghang1989/PyTorch-Encoding)]\n- [DenseASPP for Semantic Segmentation in Street Scenes - CVPR - 2018](http://openaccess.thecvf.com/content_cvpr_2018/papers/Yang_DenseASPP_for_Semantic_CVPR_2018_paper.pdf) [[code](https://github.com/DeepMotionAIResearch/DenseASPP)]\n- [PSANet: Point-wise Spatial Attention Network for Scene Parsing - ECCV - 2018](https://hszhao.github.io/papers/eccv18_psanet.pdf) [[code](https://github.com/hszhao/PSANet)]\n- [ExFuse: Enhancing Feature Fusion for Semantic Segmentation - ECCV - 2018](https://arxiv.org/abs/1804.03821)\n- [Encoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentation(DeepLabv3+) - ECCV - 2018](https://arxiv.org/abs/1804.03821) [[code](https://github.com/tensorflow/models/tree/master/research/deeplab)]\n- [Dual Attention Network for Scene Segmentation - CVPR - 2019](https://arxiv.org/abs/1809.02983) [[code](https://github.com/junfu1115/DANet)]\n- [CCNet: Criss-Cross Attention for Semantic Segmentation - ARXIV - 2018](https://arxiv.org/abs/1811.11721) [[code](https://github.com/speedinghzl/CCNet)]\n- [OCNet: Object Context Network for Scene Parsing - ARXIV - 2018](https://arxiv.org/abs/1809.00916) [[code](https://github.com/PkuRainBow/OCNet.pytorch)]\n- [Decoders Matter for Semantic Segmentation: Data-Dependent Decoding Enables Flexible Feature Aggregation - CVPR - 2019](https://arxiv.org/abs/1903.02120)\n\n### Efficient\n- [ENet: A Deep Neural Network Architecture for Real-Time Semantic Segmentation  - ARXIV - 2016](https://arxiv.org/abs/1606.02147) [[code](https://github.com/TimoSaemann/ENet)]\n- [ICNet for Real-Time Semantic Segmentation on High-Resolution Images - ECCV - 2018](https://arxiv.org/abs/1704.08545) [[code](https://github.com/hszhao/ICNet)]\n- [LinkNet: Exploiting Encoder Representations for Efficient Semantic Segmentation - ECCV - 2018](https://arxiv.org/abs/1707.03718) [[code](https://github.com/e-lab/LinkNet)]\n- [BiSeNet: Bilateral Segmentation Network for Real-time Semantic Segmentation - ECCV - 2018](https://arxiv.org/abs/1808.00897) [[code](https://github.com/ycszen/TorchSeg)]\n- [ESPNet: Efficient Spatial Pyramid of Dilated Convolutions for Semantic Segmentation - ECCV - 2018](https://arxiv.org/abs/1803.06815) [[code](https://github.com/sacmehta/ESPNet)]\n- [CGNet: A Light-weight Context Guided Network for Semantic Segmentation - ARXIV - 2018](https://arxiv.org/abs/1811.08201) [[code](https://github.com/wutianyiRosun/CGNet)]\n\n\n## Instance Segmentation\n- [Mask R-CNN - ICCV - 2017](https://arxiv.org/abs/1703.06870)\n- [Learning to Segment Every Thing - ARXIV - 2017](https://arxiv.org/abs/1711.10370)\n- [MaskLab: Instance Segmentation by Refining Object Detection with Semantic and Direction Features - CVPR - 2018](https://arxiv.org/abs/1712.04837)\n- [Path Aggregation Network for Instance Segmentation - CVPR - 2018](https://arxiv.org/abs/1803.01534) [[code](https://github.com/ShuLiu1993/PANet)]\n- [Affinity Derivation and Graph Merge for Instance Segmentation - ECCV - 2018](https://arxiv.org/abs/1811.10870)\n- [Hybrid Task Cascade for Instance Segmentation - CVPR - 2019](https://arxiv.org/abs/1901.07518v1)\n- [Mask Scoring R-CNN - CVPR - 2019](https://arxiv.org/abs/1903.00241) [[code](https://github.com/zjhuang22/maskscoring_rcnn)]\n\n### Amodal Segmentation\n\n- [Semantic Amodal Segmentation - CVPR - 2017](https://arxiv.org/pdf/1509.01329.pdf)\n- [Learning to See the Invisible: End-to-End Trainable Amodal Instance Segmentation - ARXIV - 2018](https://arxiv.org/abs/1804.08864)\n- [Amodal Instance Segmentation - ECCV - 2016](https://arxiv.org/abs/1604.08202)\n\n### Panoramic Segmentation\n\n- [Panoptic Segmentation - ARXIV - 2018](https://arxiv.org/abs/1801.00868)\n- [Weakly- and Semi-Supervised Panoptic Segmentation - ECCV -2018](http://www.robots.ox.ac.uk/~tvg/publications/2018/0095.pdf) [[code]( https://github.com/qizhuli/Weakly-Supervised-Panoptic-Segmentation)]\n- [Panoptic Segmentation with a Joint Semantic and Instance Segmentation Network - ARXIV - 2018](https://arxiv.org/abs/1809.02110)\n- [COCO18WINNER - Panoptic - Megvii](http://presentations.cocodataset.org/ECCV18/COCO18-Panoptic-Megvii.pdf)  \n- [Interactive Full Image Segmentation - ARXIV - 2018](https://arxiv.org/abs/1812.01888)\n- [Attention-guided Unified Network for Panoptic Segmentation - ARXIV - 2018](https://arxiv.org/abs/1812.03904)\n- [Learning to Fuse Things and Stuff - ARXIV - 2018]( https://arxiv.org/abs/1812.01192)\n- [Panoptic Feature Pyramid Networks - ARXIV - 2019](http://cn.arxiv.org/pdf/1901.02446v1)\n- [UPSNet: A Unified Panoptic Segmentation Network - ARXIV - 2019](https://arxiv.org/abs/1901.03784)\n- [Attention-guided Unified Network for Panoptic Segmentation - CVPR - 2019](https://arxiv.org/abs/1812.03904)\n\n### Video Segmentation\n- [One-Shot Video Object Segmentation - CVPR - 2017](https://arxiv.org/abs/1611.05198)\n- [Video Object Segmentation Without Temporal Information - T-PAMI - 2017](https://arxiv.org/abs/1709.06031v2)\n- [Online Adaptation of Convolutional Neural Networks for Video Object Segmentation - BMVC - 2017](https://arxiv.org/abs/1706.09364v2)\n- [Efficient Video Object Segmentation via Network Modulation - CVPR - 2018](https://arxiv.org/abs/1802.01218v1)\n- [Blazingly Fast Video Object Segmentation with Pixel-Wise Metric Learning - CVPR - 2018](https://arxiv.org/abs/1804.03131v1)\n- [PReMVOS: Proposal-generation, Refinement and Merging for Video Object Segmentation - ACCV - 2018](https://arxiv.org/abs/1807.09190v2)\n- [VideoMatch: Matching based Video Object Segmentation - ECCV - 2018](https://arxiv.org/abs/1809.01123v1)\n- [A Generative Appearance Model for End-to-end Video Object Segmentation - ARXIV - 2018](https://arxiv.org/abs/1811.11611v2)\n- [Meta Learning Deep Visual Words for Fast Video Object Segmentation - ARXIV - 2018](https://arxiv.org/abs/1812.01397v1)\n- [FEELVOS: Fast End-to-End Embedding Learning for Video Object Segmentation - CVPR - 2019](https://arxiv.org/abs/1902.09513)\n\n### 3D Segmentation\n\n### Related Topics -- Image Matting\n- [Deep Image Matting - CVPR - 2017](https://arxiv.org/pdf/1703.03872.pdf)\n- [A Perceptually Motivated Online Benchmark for Image Matting - CVPR - 2009](https://publik.tuwien.ac.at/files/PubDat_180666.pdf)\n- [Resources - Alpha Matting Evaluation Website](http://www.alphamatting.com/index.html)\n\n\n\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fnemonameless%2Fsegmentation-paper-list","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fnemonameless%2Fsegmentation-paper-list","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fnemonameless%2Fsegmentation-paper-list/lists"}