aobannong / Deep-Learning-Semantic-Segmentation

A paper list of semantic segmentation using deep learning.

Deep-Learning-Semantic-Segmentation

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

dataset

2D Semantic Segmentation

2014

• [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]

2015

• [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]

2016

• [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]

2017

• [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]

2018

• 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)

2019

• 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]

Real-Time Semantic Segmentation

1. [ENet] ENet: A Deep Neural Network Architecture for Real-Time Semantic Segmentation-2016 [Paper]
2. [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)
3. [RTSeg] RTSeg: Real-time Semantic Segmentation Comparative Study [Paper]
4. [ShuffleSeg] ShuffleSeg: Real-time Semantic Segmentation Network [Paper]
5. [ESPNet] [ECCV 2018] ESPNet: Efficient Spatial Pyramid of Dilated Convolutions for Semantic Segmentation [Paper]
6. [ContextNet] [BMVC 2018] ContextNet: Exploring Context and Detail for Semantic Segmentation in Real-time [Paper]
7. Guided Upsampling Network for Real-Time Semantic Segmentation [Project] [Paper]
8. [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)
9. Efficient Dense Modules of Asymmetric Convolution for Real-Time Semantic Segmentation [Paper]
10. [BMVC 2018] Light-Weight RefineNet for Real-Time Semantic Segmentation [Paper] [code]
11. CGNet: A Light-weight Context Guided Network for Semantic Segmentation [Paper] [Code]
12. ShelfNet for Real-time Semantic Segmentation [Paper]
13. ESPNetv2: A Light-weight, Power Efficient, and General Purpose Convolutional Neural Network [Paper][Code]
14. Real time backbone for semantic segmentation [Paper]
15. DSNet for Real-Time Driving Scene Semantic Segmentation [Paper]
16. In Defense of Pre-trained ImageNet Architectures for Real-time Semantic Segmentation of Road-driving Images [Paper]
17. Residual Pyramid Learning for Single-Shot Semantic Segmentation [Paper]
18. DFANet: Deep Feature Aggregation for Real-Time Semantic Segmentation [Paper]

Loss Fuction

1. The Lovász Hinge: A Novel Convex Surrogate for Submodular Losses [arxiv] [project]
2. [CVPR 2017 ] Loss Max-Pooling for Semantic Image Segmentation [Paper]
3. [CVPR 2018] The Lovász-Softmax loss：A tractable surrogate for the optimization of the intersection-over-union measure in neural networks [Project] [Paper] [Code]
4. Generalised Dice overlap as a deep learning loss function for highly unbalanced segmentations [Paper]
5. IoU is not submodular [arxiv]
6. Yes, IoU loss is submodular - as a function of the mispredictions [arxiv]
7. [BMVC 2018] NeuroIoU: Learning a Surrogate Loss for Semantic Segmentation [Paper] [code]

Review

• A Survey of Semantic Segmentation [arxiv]
• A Review on Deep Learning Techniques Applied to Semantic Segmentation [arxiv]
• Recent progress in semantic image segmentation [arxiv]