/paper-reading-list

Some papers read.

Paper-reading-list

This repository records some of papers. The synchronized blog.
The synchronized Github.

Semantic Segmentation

  • [AdaptSegNet] Learning to Adapt Structured Output Space for Semantic Segmentation-CVPR2018<Paper><Code-Pytorch>
  • [DAM/DCM] Unsupervised Cross-Modality Domain Adaptation of ConvNets for Biomedical Image Segmentations with Adversarial Loss-IJCAI2018<Paper>
  • [FCAN] Fully Convolutional Adaptation Networks for Semantic Segmentation-CVPR2018<Paper>
  • [DenseASPP] DenseASPP for Semantic Segmentation in Street Scenes-CVPR2018<Paper><Code-Pytorch>
  • Dense Decoder Shortcut Connections for Single-Pass Semantic Segmentation-CVPR2018<Paper>
  • [AotofocusLayer] Autofocus Layer for Semantic Segmentation-MICCAI2018<Paper><Code-Pytorch>
  • [PDV-Net] Automatic Segmentation of Pulmonary Lobes Using a Progressive Dense V-Network-MICCAI2018<Paper>
  • [RR-SegSE] Adaptive feature recombination and recalibration for semantic segmentation: application to brain tumor segmentation in MRI-MICCAI2018<Paper><Code>
  • [HD-Net] Fine-Grained Segmentation Using Hierarchical Dilated Neural Networks-MICCAI2018<Paper>
  • [U-JAPA-Net] 3D U-JAPA-Net: Mixture of Convolutional Networks for Abdominal Multi-organ CT Segmentation-MICCAI2018<Paper>
  • .[CompNet] CompNet: Complementary Segmentation Network for Brain MRI Extraction-MICCAI2018<Paper><Code-Keras>
  • Deep Learning-Based Boundary Detection for Model-Based Segmentation with Application to MR Prostate Segmentation-MICCAI2018<Paper>
  • [RS-Net] RS-Net: Regression-Segmentation 3D CNN for Synthesis of Full Resolution Missing Brain MRI in the Presence of Tumours-MICCAI2018<Paper><Code>
  • CT-Realistic Lung Nodule Simulation from 3D Conditional Generative Adversarial Networks for Robust Lung Segmentation-MICCAI2018<Paper>
  • [CB-GANs] Learning Data Augmentation for Brain Tumor Segmentation with Coarse-to-Fine Generative Adversarial Networks-MICCAI2018<Paper>
  • [FSENet] Focus, Segment and Erase: An Efficient Network for Multi-Label Brain Tumor Segmentation-ECCV2018<Paper><Code-Pytorch>
  • [DeepLabv3+] Encoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentation-ECCV2018<Paper><Code-Tensorflow>
  • [ExFuse] ExFuse: Enhancing Feature Fusion for Semantic Segmentation-ECCV2018<Paper>
  • [ESPNet] ESPNet: Efficient Spatial Pyramid of Dilated Convolutions for Semantic Segmentation-ECCV2018<Paper><Code-Pytorch>
  • [EncNet] Context Encoding for Semantic Segmentation-CVPR2018<Paper><Code-Pytorch>
  • [PSPNet] Pyramid Scene Parsing Network-CVPR2017<Paper><Code-Caffe>
  • [DANet] Dual Attention Network for Scene Segmentation-CVPR2019<Paper><Code-PyTorch>
  • [BiSeNet] BiSeNet: Bilateral Segmentation Network for Real-time Semantic Segmentation-ECCV-2018<Paper><Code-PyTorch>
  • [Fast-SCNN] Fast-SCNN: Fast Semantic Segmentation Network-2019<Paper><Code-PyTorch>
  • [ICNet] ICNet for Real-Time Semantic Segmentation on High-Resolution Images-ECCV2018<Paper><Code-PyTorch><Code-Caffe>
  • [DUNet] Decoders Matter for Semantic Segmentation: Data-Dependent Decoding Enables Flexible Feature Aggregation-CVPR2019<Paper><Code-PyTorch>
  • [ENet] ENet: A Deep Neural Network Architecture for Real-Time Semantic Segmentation-2016<Paper><Code-Caffe><Code-PyTorch><Code-Tensorflow>

Panoptic Segmentation

  • [Panoptic FPN] Panoptic Feature Pyramid Networks-Arxiv2019<Paper>

Super-Resolution

  • [mDCSRN] Efficient and Accurate MRI Super-Resolution using a Generative Adversarial Network and 3D Multi-Level Densely Connected Network-MICCAI2018<Paper>
  • [RDN] Residual Dense Network for Image Super-Resolution-CVPR2018<Paper><Code-Torch><Code-Pytorch><Code-Tensorflow>

Loss Function

Networks Architecture

  • [DLA] Deep Layer Aggregation-CVPR2018<Paper><Code-Pytorch>
  • [DualSkipNet] Dual Skipping Networks-CVPR2018<Paper>
  • [SkipNet] SkipNet: Learning Dynamic Routing in Convolutional Networks-ECCV2018<Paper><Code-Pytorch>
  • [DRN] Dilated Residual Networks-CVPR2017<Paper><Code-Pytorch>
  • [CapsNet] Dynamic Routing Between Capsules-NIPS2017<Paper><Code-Tensorflow>
  • [BlockQNN] Practical Block-wise Neural Network Architecture Generation-CVPR2018<Paper>
  • [MobileNetV2] MobileNetV2: Inverted Residuals and Linear Bottlenecks-CVPR2018<Paper><Code-Tensorflow>
  • [Non-Local] Non-local Neural Networks-CVPR2018<Paper><Code>