Pinned Repositories
AdaptSegNet
Learning to Adapt Structured Output Space for Semantic Segmentation, CVPR 2018 (spotlight)
Cpp_Primer_Practice
搞定C++:punch:。C++ Primer 中文版第5版学习仓库，包括笔记和课后练习答案。
Dilation-Pytorch-Semantic-Segmentation
A PyTorch implementation of semantic segmentation according to Multi-Scale Context Aggregation by Dilated Convolutions by Yu and Koltun.
FCN
FCNonKitti-Cityscapes
This is the basic fcn model which trained on KITTI2015/Cityscapes and based on PyTorch
GCnet-pytorch
This is a pytorch type of block，including Non-local block，Simple Non-local block，GC block and all GC block; refer to paper《GCNet: Non-local Networks Meet Squeeze-Excitation Networks and Beyond》
kitti-stereo-devkit-python
KITTI Optical Flow Evaluation 2015 devkit, but with python
MCD_DA
Pixel-Level-Cycle-Association
Pytorch Implementation for NeurIPS (oral) paper: Pixel Level Cycle Association: A New Perspective for Domain Adaptive Semantic Segmentation
ProDA
Prototypical Pseudo Label Denoising and Target Structure Learning for Domain Adaptive Semantic Segmentation (CVPR 2021)
JiangXiaobai00's Repositories
JiangXiaobai00/FCN
JiangXiaobai00/GCnet-pytorch
This is a pytorch type of block，including Non-local block，Simple Non-local block，GC block and all GC block; refer to paper《GCNet: Non-local Networks Meet Squeeze-Excitation Networks and Beyond》
JiangXiaobai00/Awesome-pytorch-list
A comprehensive list of pytorch related content on github,such as different models,implementations,helper libraries,tutorials etc.
JiangXiaobai00/awesome-semantic-segmentation-pytorch
Semantic Segmentation on PyTorch (include FCN, PSPNet, Deeplabv3, DANet, DenseASPP, BiSeNet, EncNet, DUNet, ICNet, ENet, OCNet, CCNet, PSANet, CGNet, ESPNet, LEDNet)
JiangXiaobai00/code-of-learn-deep-learning-with-pytorch
This is code of book "Learn Deep Learning with PyTorch"
JiangXiaobai00/daily-paper-computer-vision
记录每天整理的计算机视觉/深度学习/机器学习相关方向的论文
JiangXiaobai00/dec-pytorch
JiangXiaobai00/DeepClustering
A pytorch implementation of the paper Unsupervised Deep Embedding for Clustering Analysis.
JiangXiaobai00/Deeplab_pytorch
All version of deeplab implemented in Pytorch
JiangXiaobai00/edlsm_pytorch
This is a Pytorch implementation for stereo matching described in the paper: Efficient Deep learning for stereo matching
JiangXiaobai00/eesen
The official repository of the Eesen project
JiangXiaobai00/FCN-pytorch
🚘 Easiest Fully Convolutional Networks
JiangXiaobai00/fcn.berkeleyvision.org
Fully Convolutional Networks for Semantic Segmentation by Jonathan Long*, Evan Shelhamer*, and Trevor Darrell. CVPR 2015 and PAMI 2016.
JiangXiaobai00/fcn.pytorch
JiangXiaobai00/GC-Net
gc-net for stereo matching by using pytorch
JiangXiaobai00/monodepth
Unsupervised Monocular Depth Estimation with Left-Right Consistency
JiangXiaobai00/MonoDepth-PyTorch
Unofficial implementation of Unsupervised Monocular Depth Estimation neural network MonoDepth in PyTorch
JiangXiaobai00/Neural_Color_Transfer
Implementation of Neural Color Transfer between Images by PyTorch.
JiangXiaobai00/open-source-badges
:octocat: Open Source & Licence Badges
JiangXiaobai00/PSMNet
A Pytorch implementation of Pyramid Stereo Matching Network
JiangXiaobai00/PSMNet-1
Pyramid Stereo Matching Network (CVPR2018)
JiangXiaobai00/PSMNet-Tensorflow
Pyramid Stereo Matching Network-tensorflow
JiangXiaobai00/pytorch-fcn
PyTorch Implementation of Fully Convolutional Networks. (Training code to reproduce the original result is available.)
JiangXiaobai00/pytorch_fcn
implementation of FCN with pytorch
JiangXiaobai00/Real-time-self-adaptive-deep-stereo
Code for "Real-time self-adaptive deep stereo" - CVPR 2019 (ORAL)
JiangXiaobai00/scikit-image
Image processing in Python
JiangXiaobai00/Segmentation-MonoDepth-Pytorch
CNNs for semantic segmentation and monocular depth estimation in Pytorch with cross experiments and saliency map analysis
JiangXiaobai00/SegStereo
SegStereo: Exploiting Semantic Information for Disparity Estimation
JiangXiaobai00/Style-transfer-with-neural-algorithm
Implementation of style transfer by tensorflow, for detail please see the paper "Image Style Transfer Using Convolutional Neural Networks"(CVPR2016)
JiangXiaobai00/Style_Transfer_With_Deep_Neural_Networks_In_PyTorch
In this project, I recreated a style transfer method that is outlined in the paper, [Image Style Transfer Using Convolutional Neural Networks, by Gatys](https://www.cv-foundation.org/openaccess/content_cvpr_2016/papers/Gatys_Image_Style_Transfer_CVPR_2016_paper.pdf) in PyTorch. In the paper, style transfer uses the features found in the 19-layer VGG Network, which is comprised of a series of convolutional and pooling layers, and a few fully-connected layers.