Efficient Piecewise Training of Deep Structured Models for Semantic Segmentation

Very Deep Convolutional Networks for Large-Scale Image Recognition

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Microsoft COCO: Common Objects in Context

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Learning Deconvolution Network for Semantic Segmentation

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Semantic Image Segmentation with Deep Convolutional Nets and Fully Connected CRFs

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Depth Map Prediction from a Single Image using a Multi-Scale Deep Network

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Efficient Inference in Fully Connected CRFs with Gaussian Edge Potentials

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Predicting Depth, Surface Normals and Semantic Labels with a Common Multi-scale Convolutional Architecture

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Learning Hierarchical Features for Scene Labeling

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Conditional Random Fields as Recurrent Neural Networks

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SIFT Flow: Dense Correspondence across Scenes and Its Applications

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Learning Rich Features from RGB-D Images for Object Detection and Segmentation

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The Role of Context for Object Detection and Semantic Segmentation in the Wild

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Joint Training of a Convolutional Network and a Graphical Model for Human Pose EstimationPreprint

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Learning Depth from Single Monocular Images Using Deep Convolutional Neural Fields

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Simultaneous Detection and Segmentation

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BoxSup: Exploiting Bounding Boxes to Supervise Convolutional Networks for Semantic Segmentation

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Deep convolutional neural fields for depth estimation from a single image

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MatConvNet - Convolutional Neural Networks for MATLAB

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Semantic Image Segmentation via Deep Parsing Network

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Recurrent Convolutional Neural Networks for Scene Labeling

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Perceptual Organization and Recognition of Indoor Scenes from RGB-D Images

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Semantic Segmentation with Second-Order Pooling

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Restoring an Image Taken through a Window Covered with Dirt or Rain

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Feedforward semantic segmentation with zoom-out features

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Convolutional feature masking for joint object and stuff segmentation

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Learning Spatial Context: Using Stuff to Find Things

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The Layout Consistent Random Field for Recognizing and Segmenting Partially Occluded Objects

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Structured Learning and Prediction in Computer Vision

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Fully Connected Deep Structured NetworksPreprint

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Finding Things: Image Parsing with Regions and Per-Exemplar Detectors

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Piecewise Training for Undirected ModelsPreprint

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Learning Deep Structured ModelsPreprint

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Recurrent Convolutional Neural Networks for Scene ParsingPreprint

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Closed-Form Training of Conditional Random Fields for Large Scale Image SegmentationPreprint

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DeepLab: Semantic Image Segmentation with Deep Convolutional Nets, Atrous Convolution, and Fully Connected CRFs

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SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentation

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Pyramid Scene Parsing Network

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Encoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentation

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The Cityscapes Dataset for Semantic Urban Scene Understanding

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Dual Attention Network for Scene Segmentation

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Deep High-Resolution Representation Learning for Visual Recognition

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Image segmentation using deep learning: A survey

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Multi-task Learning Using Uncertainty to Weigh Losses for Scene Geometry and Semantics

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RefineNet: Multi-path Refinement Networks for High-Resolution Semantic Segmentation

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BiSeNet: Bilateral Segmentation Network for Real-Time Semantic Segmentation

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Deep learning for visual understanding: A review

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Conditional Random Fields as Recurrent Neural Networks

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Understanding Convolution for Semantic Segmentation

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Learning to Adapt Structured Output Space for Semantic Segmentation

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Large Kernel Matters — Improve Semantic Segmentation by Global Convolutional Network

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A survey on deep learning and its applications

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Large-Scale Point Cloud Semantic Segmentation with Superpoint Graphs

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Context Encoding for Semantic Segmentation

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ERFNet: Efficient Residual Factorized ConvNet for Real-Time Semantic Segmentation

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Attention to Scale: Scale-Aware Semantic Image Segmentation

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The Mapillary Vistas Dataset for Semantic Understanding of Street Scenes

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PSANet: Point-wise Spatial Attention Network for Scene Parsing

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DeepCrack: A deep hierarchical feature learning architecture for crack segmentation

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Structural-RNN: Deep Learning on Spatio-Temporal Graphs

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Fully Convolutional Instance-Aware Semantic Segmentation

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ParseNet: Looking Wider to See BetterPreprint

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Learning a Discriminative Feature Network for Semantic Segmentation

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Learning Pixel-Level Semantic Affinity with Image-Level Supervision for Weakly Supervised Semantic Segmentation

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FuseNet: Incorporating Depth into Semantic Segmentation via Fusion-Based CNN Architecture

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Strip Pooling: Rethinking Spatial Pooling for Scene Parsing

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Gated-SCNN: Gated Shape CNNs for Semantic Segmentation

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A review of semantic segmentation using deep neural networks

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Simple Does It: Weakly Supervised Instance and Semantic Segmentation

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Asymmetric Non-Local Neural Networks for Semantic Segmentation

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CANet: Class-Agnostic Segmentation Networks With Iterative Refinement and Attentive Few-Shot Learning

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DASNet: Dual Attentive Fully Convolutional Siamese Networks for Change Detection in High-Resolution Satellite Images

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Beyond RGB: Very high resolution urban remote sensing with multimodal deep networks

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High-Resolution Representations for Labeling Pixels and RegionsPreprint

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SegAN: Adversarial Network with Multi-scale L1 Loss for Medical Image Segmentation

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Full-Resolution Residual Networks for Semantic Segmentation in Street Scenes

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A Brief Survey on Semantic Segmentation with Deep Learning

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CDC: Convolutional-De-Convolutional Networks for Precise Temporal Action Localization in Untrimmed Videos

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Survey on semantic segmentation using deep learning techniques

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PAD-Net: Multi-tasks Guided Prediction-and-Distillation Network for Simultaneous Depth Estimation and Scene Parsing

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3D Graph Neural Networks for RGBD Semantic Segmentation

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DeepIGeoS: A Deep Interactive Geodesic Framework for Medical Image Segmentation

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Recent progress in semantic image segmentation

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Laplacian Pyramid Reconstruction and Refinement for Semantic Segmentation

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MA-Net: A Multi-Scale Attention Network for Liver and Tumor Segmentation

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