A paper list of semantic segmentation using deep learning.
network | VOC12 | VOC12 with COCO | Pascal Context | CamVid | Cityscapes | ADE20K | Published In |
---|---|---|---|---|---|---|---|
FCN-8s | 62.2 | 37.8 | 65.3 | CVPR 2015 | |||
DeepLab | 71.6 | ICLR 2015 | |||||
CRF-RNN | 72.0 | 74.7 | 39.3 | ICCV 2015 | |||
DeconvNet | 72.5 | ICCV 2015 | |||||
DPN | 74.1 | 77.5 | ICCV 2015 | ||||
SegNet | 50.2 | ||||||
Dilation8 | 75.3 | ||||||
Deeplab v2 | 79.7 | 45.7 | 70.4 | PAMI | |||
FRRN B | 71.8 | CVPR 2017 | |||||
G-FRNet | 79.3 | 68.0 | CVPR 2017 | ||||
GCN | 82.2 | 76.9 | CVPR 2017 | ||||
SegModel | 82.5 | 79.2 | CVPR 2017 | ||||
RefineNet | 83.4 | 47.3 | 73.6 | 40.7 | CVPR 2017 | ||
PSPNet | 82.6 | 85.4 | 80.2 | CVPR 2017 | |||
DIS | 86.8 | ICCV 2017 | |||||
SAC-multiple | 78.1 | 44.3 | ICCV 2017 | ||||
DeepLabv3 | 85.7 | 81.3 | arxiv 1706.05587 | ||||
DUC-HDC | 80.1 | WACV2018 | |||||
DDSC | 81.2 | 47.8 | 70.9 | CVPR 2018 | |||
EncNet | 82.9 | 85.9 | 51.7 | 44.65 | CVPR 2018 | ||
DFN | 82.7 | 86.2 | 80.3 | CVPR 2018 | |||
DenseASPP | 80.6 | CVPR 2018 | |||||
UperNet | 42.66 | ECCV 2018 | |||||
PSANet | 85.7 | 80.1 | 43.77 | ECCV 2018 | |||
DeepLabv3+ | 87.8 | 82.1 | ECCV 2018 | ||||
ExFuse | 87.9 | ECCV 2018 | |||||
OCNet | 81.2(81.7) | 45.08(45.45) | arxiv 1809.00916 | ||||
DAN | 52.6 | 78.2 | CVPR 2019 | ||||
DPC | 87.9 | 82.7 | NIPS 2018 | ||||
CCNet | 81.4 | 45.22 | arxiv 1811.11721 | ||||
GloRe | 80.9 | CVPR 2019 | |||||
TKCN | 83.2 | 79.5 | ICME 2019 | ||||
GCU | 44.81 | NIPS 2018 | |||||
DUpsampling | 85.3 | 88.1 | 52.5 | CVPR 2019 | |||
FastFCN | 53.1 | 44.34 | arxiv 1903.11816 | ||||
GFF | 82.3 | 45.33 | arxiv 1904.01803 | ||||
HRNetV2 | 54.0 | 81.6 | arxiv 1904.04514 | ||||
CaseNet | 81.9 | 45.28 | arxiv 1904.08170 |
Semantic Segmentation论文整理
- [FCN] Fully Convolutional Networks for Semantic Segmentation [Paper1] [Paper2] [Slides1] [Slides2]
- [DeepLab v1] Semantic Image Segmentation With Deep Convolutional Nets and Fully Connected CRFs[Code-Caffe] [Paper]
- [CRF as RNN] Conditional Random Fields as Recurrent Neural Networks [Project] [Demo] [Paper]
- [DeconvNet] Learning Deconvolution Network for Semantic Segmentation [Project] [Paper] [Slides]
- [U-Net] U-Net: Convolutional Networks for Biomedical Image Segmentation [Project] [Paper]
- [SegNet] SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentation [Project] [Paper] [Tutorial1] [Tutorial2]
- Multi-scale context aggregation by dilated convolutions [Paper]
- [DeepLab v2] DeepLab v2:Semantic Image Segmentation with Deep Convolutional Nets, Atrous Convolution, and Fully Connected CRFs** [Project] [Code-Caffe] [Paper]
- [RefineNet] [CVPR2017] RefineNet: Multi-Path Refinement Networks for High-Resolution Semantic Segmentation [Code-MatConvNet] [Paper]
- [IFCN] Improving Fully Convolution Network for Semantic Segmentation [Paper]
- [FC-DenseNet] [CVPRW2017] The One Hundred Layers Tiramisu: Fully Convolutional DenseNets for Semantic Segmentation [Code-Theano] [Code-Keras1] [Code-Keras2] [Paper]
- [PSPNet] [CVPR2017] Pyramid Scene Parsing Network [Project] [Code-Caffe] [Paper] [Slides]
- [FusionNet] FusionNet: A deep fully residual convolutional neural network for image segmentation in connectomics [Code-PyTorch] [Paper]
- [PixelNet] PixelNet: Representation of the pixels, by the pixels, and for the pixels [Project] [Code-Caffe] [Paper]
- [DUC-HDC] [WACV 2018]Understanding Convolution for Semantic Segmentation [Model-Mxnet] [Paper] [Code]
- [GCN] [CVPR2017] Large Kernel Matters - Improve Semantic Segmentation by Global Convolutional Network [Paper]
- [CVPR 2017] Not All Pixels Are Equal: Difficulty-Aware Semantic Segmentation via Deep Layer Cascade-2017 [Paper]
- Pixel Deconvolutional Networks-2017 [Code-Tensorflow] [Paper]
- [DRN] [CVPR 2017] Dilated Residual Networks [Paper] [Code]
- [Deeplab v3] Deeplab v3: Rethinking Atrous Convolution for Semantic Image Segmentation [Paper]
- [LinkNet] LinkNet: Exploiting Encoder Representations for Efficient Semantic Segmentation [Paper]
- [SDN] Stacked Deconvolutional Network for Semantic Segmentation [Paper]
- Learning to Segment Every Thing [Paper]
- Panoptic Segmentation [Paper]
- [DeepLabv3+] [ECCV 2018] Encoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentation [Paper] [Code]
- [EncNet] [CVPR 2018] Context Encoding for Semantic Segmentation [Paper] [Code] (Leverages global context to increase accuracy by adding a channel attention module, which triggers attention on certain feature maps based on a newly designed loss function. The loss is based on a network branch which predicts which classes are present in the image (i.e higher level global context))
- [ECCV 2018] Adaptive Affinity Fields for Semantic Segmentation [Project] [Paper] [Code]
- [EXFuse] [ECCV 2018] ExFuse: Enhancing Feature Fusion for Semantic Segmentation [Paper] (Uses deep supervision and explicitly combines the multi-scale features from the feature extraction frontend before processing, in order to ensure multi-scale information is processed together at all levels)
- Vortex Pooling: Improving Context Representation in Semantic Segmentation [Paper]
- [DFN] [CVPR 2018] Learning a Discriminative Feature Network for Semantic Segmentation [Paper] (Uses deep supervision and attempts to process the smooth and edge portions of the segments separately)
- Stacked U-Nets: A No-Frills Approach to Natural Image Segmentation [Paper]
- [BMVC 2018] Pyramid Attention Network for Semantic Segmentation [Paper]
- [G-FRNet] [CVPR 2017] Gated Feedback Refinement Network for Coarse-to-Fine Dense Semantic Image Labeling [Paper] [code]
- [CVPR 2018] Context Contrasted Feature and Gated Multi-Scale Aggregation for Scene Segmentation [Paper]
- [DenseASPP] [CVPR 2018] DenseASPP for Semantic Segmentation in Street Scenes [Paper] [code] (Combines dense connections with atrous convolutions)
- [CVPR 2018] Dense Decoder Shortcut Connections for Single-Pass Semantic Segmentation [Paper] (Use dense connections in the decoding stage for higher accuracy (previously only done during feature extraction / encoding))
- Smoothed Dilated Convolutions for Improved Dense Prediction [Paper]
- [PSANet] [ECCV 2018] PSANet: Point-wise Spatial Attention Network for Scene Parsing [Paper] [project] [code] [slide] (Attention Mechanism)
- [OCNet] OCNet: Object Context Network for Scene Parsing [Paper] [code] (Attention Mechanism)
- [DAN] [CVPR 2019] Dual Attention Network for Scene Segmentation [Paper] [code] (Attention Mechanism)
- [CCNet] CCNet: Criss-Cross Attention for Semantic Segmentation [Paper] [code] (Attention Mechanism)
- [GloRe] [CVPR 2019] Graph-Based Global Reasoning Networks [Paper] (Graph Convolution)
- [TKCN] Tree-structured Kronecker Convolutional Networks for Semantic Segmentation [Paper] [code]
- [GCU] Beyond Grids: Learning Graph Representations for Visual Recognition [Paper] (Graph Convolution)
- Auto-DeepLab: Hierarchical Neural Architecture Search for Semantic Image Segmentation[Paper]
- Decoders Matter for Semantic Segmentation: Data-Dependent Decoding Enables Flexible Feature Aggregation [Paper]
- [CVPR 2019] Structured Knowledge Distillation for Semantic Segmentation [Paper]
- [CVPR 2019] Knowledge Adaptation for Efficient Semantic Segmentation [Paper]
- [CVPR 2019] A Cross-Season Correspondence Dataset for Robust Semantic Segmentation [Paper]
- Efficient Smoothing of Dilated Convolutions for Image Segmentation [Paper] [[Code]](https://github.com/ThomasZiegler/Efficient-Smoothing-of-DilaBeyond GridsBeyond GridsBeyond GridsBeyondted-Convolutions)
- [FastFCN] FastFCN:Rethinking Dilated Convolution in the Backbone for Semantic Segmentation [Paper] [Code]
- [GFF] GFF: Gated Fully Fusion for Semantic Segmentation [Paper]
- DADA: Depth-aware Domain Adaptation in Semantic Segmentation [Paper]
- [HRNetV2] High-Resolution Representations for Labeling Pixels and Regions [Paper]
- CaseNet: Content-Adaptive Scale Interaction Networks for Scene Parsing [Paper]
- Student Becoming the Master: Knowledge Amalgamation for Joint Scene Parsing, Depth Estimation, and More [Paper]
- [CVPR 2019] Bidirectional Learning for Domain Adaptation of Semantic Segmentation [Paper]
- [CVPR 2019] Box-driven Class-wise Region Masking and Filling Rate Guided Loss for Weakly Supervised Semantic Segmentation [Paper]
- [ENet] ENet: A Deep Neural Network Architecture for Real-Time Semantic Segmentation-2016 [Paper]
- [ICNet] [ECCV 2018] ICNet for Real-Time Semantic Segmentation on High-Resolution Images [Project] [Code] [Paper] [Video] (Uses deep supervision and runs the input image at different scales, each scale through their own subnetwork and progressively combining the results)
- [RTSeg] RTSeg: Real-time Semantic Segmentation Comparative Study [Paper]
- [ShuffleSeg] ShuffleSeg: Real-time Semantic Segmentation Network [Paper]
- [ESPNet] [ECCV 2018] ESPNet: Efficient Spatial Pyramid of Dilated Convolutions for Semantic Segmentation [Paper]
- [ContextNet] [BMVC 2018] ContextNet: Exploring Context and Detail for Semantic Segmentation in Real-time [Paper]
- Guided Upsampling Network for Real-Time Semantic Segmentation [Project] [Paper]
- [BiSeNet] [ECCV 2018] BiSeNet: Bilateral Segmentation Network for Real-time Semantic Segmentation [Paper] (Has 2 branches: one is deep for getting semantic information, while the other does very little / minor processing on the input image as to preserve the low-level pixel information)
- Efficient Dense Modules of Asymmetric Convolution for Real-Time Semantic Segmentation [Paper]
- [BMVC 2018] Light-Weight RefineNet for Real-Time Semantic Segmentation [Paper] [code]
- CGNet: A Light-weight Context Guided Network for Semantic Segmentation [Paper] [Code]
ShelfNet for Real-time Semantic Segmentation [Paper]- ESPNetv2: A Light-weight, Power Efficient, and General Purpose Convolutional Neural Network [Paper][Code]
- Real time backbone for semantic segmentation [Paper]
- DSNet for Real-Time Driving Scene Semantic Segmentation [Paper]
- In Defense of Pre-trained ImageNet Architectures for Real-time Semantic Segmentation of Road-driving Images [Paper]
- Residual Pyramid Learning for Single-Shot Semantic Segmentation [Paper]
- DFANet: Deep Feature Aggregation for Real-Time Semantic Segmentation [Paper]
- The Lovász Hinge: A Novel Convex Surrogate for Submodular Losses [arxiv] [project]
- [CVPR 2017 ] Loss Max-Pooling for Semantic Image Segmentation [Paper]
- [CVPR 2018] The Lovász-Softmax loss:A tractable surrogate for the optimization of the intersection-over-union measure in neural networks [Project] [Paper] [Code]
- Generalised Dice overlap as a deep learning loss function for highly unbalanced segmentations [Paper]
- IoU is not submodular [arxiv]
- Yes, IoU loss is submodular - as a function of the mispredictions [arxiv]
- [BMVC 2018] NeuroIoU: Learning a Surrogate Loss for Semantic Segmentation [Paper] [code]