- Handwritten Digit Recognition with a Back-Propagation Network(LeNet) [paper]
- ImageNet Classification with Deep Convolutional Neural Networks(AlexNet) [paper]
- Deep Sparse Rectifier Neural Networks(ReLU) paper
- Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift(Batch-Norm) [paper]
- Dropout: A Simple Way to Prevent Neural Networks from Overfitting(Dropout) [paper]
- Spatial Pyramid Pooling in Deep Convolutional Networks for Visual Recognition(SPP) [paper]
- Very Deep Convolutional Networks For Large-Scale Image Recognition(VGG) [paper]
- Network In Network
- Highway Networks
- Going Deeper with Convolutions(GoogleNet)
- Rethinking the Inception Architecture for Computer Vision(Inception v3) [paper]
- PolyNet: A Pursuit of Structural Diversity in Very Deep Networks(PolyNet) [paper]
- PVANET: Deep but Lightweight Neural Networks for Real-time Object Detection(PVANet) [paper]
- Deep Residual Learning for Image Recognition(ResNet)
- Identity Mappings in Deep Residual Networks
- Wide Residual Networks(Wide-ResNet)
- Aggregated Residual Transformations for Deep Neural Networks
- Xception: Deep Learning with Depthwise Separable Convolutions(Xception) [paper]
- Densely Connected Convolutional Networks(DenseNet)
- Squeeze-and-Excitation Networks(SENet) [paper]
- MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications(MobileNet) [paper]
- ShuffleNet: An Extremely Efficient Convolutional Neural Network for Mobile Devices(ShuffleNet) [paper]
- Rich feature hierarchies for accurate object detection and semantic segmentation(RCNN)
- Fast R-CNN
- Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks
- DenseBox: Unifying Landmark Localization with End to End Object Detection(DenseBox) [paper]
- You Only Look Once: Unified, Real-Time Object Detection(YOLO) [paper]
- SSD: Single Shot MultiBox Detector(SSD) [paper]
- DSSD : Deconvolutional Single Shot Detector(DSSD) [paper]
- R-FCN: Object Detection via Region-based Fully Convolutional Networks(RFCN) [paper]
- Feature Pyramid Networks for Object Detection(FPN) [paper]
- Mask R-CNN [paper]
- Focal Loss for Dense Object Detection(RetinaNet) [paper]
- RON: Reverse Connection with Objectness Prior Networks for Object Detection(RON) [paper]
- Deformable Convolutional Networks [paper]
- Single-Shot Refinement Neural Network for Object Detection [paper]
- Light-Head R-CNN: In Defense of Two-Stage Object Detector [paper]
- Fully Convolutional Networks for Semantic Segmentation(FCN)
- Learning Deconvolution Network for Semantic Segmentation(Deconv)
- Efficient Inference in Fully Connected CRFs with Gaussian Edge Potentials
- Conditional Random Fields as Recurrent Neural Networks(CRFasRNN)
- Semantic Image Segmentation via Deep Parsing Network(DPN)
- Efficient Piecewise Training of Deep Structured Models for Semantic Segmentation
- Exploring Context with Deep Structured models for Semantic Segmentation
- Semantic Image Segmentation with Deep Convolutional Nets and Fully Connected CRFs(Deeplab v1)
- DeepLab: Semantic Image Segmentation with Deep Convolutional Nets, Atrous Convolution,and Fully Connected CRFs(Deeplab v2)
- RefineNet: Multi-Path Refinement Networks for High-Resolution Semantic Segmentation(RefineNet)
- Understanding Convolution for Semantic Segmentation(DUC)
- Wider or Deeper: Revisiting the ResNet Model for Visual Recognition
- Not All Pixels Are Equal: Difficulty-aware Semantic Segmentation via Deep Layer Cascade
- Loss Max-Pooling for Semantic Image Segmentation
- Pyramid Scene Parsing Network(PSPNet)
- Large Kernel Matters -- Improve Semantic Segmentation by Global Convolutional Network(GCN)
- Rethinking Atrous Convolution for Semantic Image Segmentation(Deeplab v3)
- Global-residual and Local-boundary Refinement Networks for Rectifying Scene Parsing Predictions
- Stacked Deconvolutional Network for Semantic Segmentation(SDN)
- Learning a Discriminative Feature Network for Semantic Segmentation(DFN)[paper]
- DenseASPP for Semantic Segmentation in Street Scenes
- Context Encoding for Semantic Segmentation
- Dynamic-structured Semantic Propagation Network
- The Lovasz-Softmax loss: A tractable surrogate for the optimization of the intersection-over-union measure in neural networks
- Encoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentation
- Instance-aware Semantic Segmentation via Multi-task Network Cascades(MNC) [paper]
- Proposal-free Network for Instance-level Object Segmentation [paper]
- Learning to Segment Object Candidates(DeepMask) [paper]
- Learning to Refine Object Segments(SharpMask) [paper]
- FastMask: Segment Multi-scale Object Candidates in One Shot(FastMask) [paper]
- Instance-sensitive Fully Convolutional Networks(Instance-sensitive FCN) [paper]
- Associative Embedding: End-to-End Learning for Joint Detection and Grouping [paper]
- Fully Convolutional Instance-aware Semantic Segmentation(FCIS) [paper]
- Mask R-CNN [paper]
- Learning to Segment Every Thing [paper]
- MaskLab: Instance Segmentation by Refining Object Detection with Semantic and Direction Features [paper]
- SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentation(SegNet)
- ENet: A Deep Neural Network Architecture for Real-Time Semantic Segmentation(ENet)
- ICNet for Real-Time Semantic Segmentation(ICNet)
- LinkNet: Exploiting Encoder Representations for Efficient Semantic Segmentation(ICNet)
- Efficient ConvNet for real-Time semantic segmentation
- Real-time Semantic Image Segmentation via Spatial Sparsity
- Video Propagation Networks
- One-Shot Video Object Segmentation
- Learning Video Object Segmentation from Static Images
- SegFlow: Joint Learning for Video Object Segmentation and Optical Flow
- Online Adaptation of Convolutional Neural Networks for Video Object Segmentation
- Lucid Data Dreaming for Object Tracking
- Lucid Data Dreaming for Multiple Object Tracking
- Video Object Segmentation with Re-identification
- Online Adaptation of Convolutional Neural Networks for Video Object Segmentation
- Learning to Segment Instances in Videos with Spatial Propagation Network
- Efficient Video Object Segmentation via Network Modulation
- Weakly- and Semi-Supervised Learning of a Deep Convolutional Network for Semantic Image Segmentation
- BoxSup: Exploiting Bounding Boxes to Supervise Convolutional Networks for Semantic Segmentation
- Constrained Convolutional Neural Networks for Weakly Supervised Segmentation
- Augmented Feedback in Semantic Segmentation under Image Level Supervision
- Webly Supervised Semantic Segmentation
- Object Region Mining with Adversarial Erasing: A Simple Classification to Semantic Segmentation Approach
- Exploiting Saliency for Object Segmentation from Image Level Labels
- Discovering Class-Specific Pixels for Weakly-Supervised Semantic Segmentation
- A Model of Saliency-based Visual Attention for Rapid Scene Analysis [paper]
- Saliency Detection: A Spectral Residual Approach
- Large-Scale Optimization of Hierarchical Features for Saliency Prediction in Natural Images(eDN)
- SALICON: Reducing the Semantic Gap in Saliency Prediction by Adapting Deep Neural Networks
- SALICON: Saliency in Context Ming
- Recurrent Attentional Networks for Saliency Detection
- DHSNet: Deep Hierarchical Saliency Network for Salient Object Detection
- Deeply supervised salient object detection with short connections
- What do different evaluation metrics tell us about saliency models?
- Deep Level Sets for Salient Object Detection Ping
- Non-Local Deep Features for Salient Object Detection
- A Stagewise Refinement Model for Detecting Salient Objects in Images
- Amulet: Aggregating Multi-level Convolutional Features for Salient Object Detection
- Deep Contrast Learning for Salient Object Detection
- Instance-Level Salient Object Segmentation
- S4Net: Single Stage Salient-Instance Segmentation
- Salient Object Detection: A Survey
- Salient Object Detection: A Benchmark
- Keras [docs]
- Caffe [install&docs]
- Caffe2 [install&docs]
- PyTorch [install] [docs]
- Mxnet/Gluon [install&docs]
- TensorFlow [install&docs]
- git
- python tutorial
- tmux
- vim
- markdown
- latex