Xu Jun's repositories
Open-Image-Enhancement
Image Enhancement Techniques for low-light/non-uniform illuminance images
illuminant_estimation
Deep Specialized Network for Illuminant Estimation
awesome-zero-shot-learning
A curated list of papers, code and resources pertaining to zero shot learning
pytorch-book
PyTorch tutorials and fun projects including neural talk, neural style, poem writing, anime generation
amc-compressed-models
[ECCV 2018] Compressed models from AMC: AutoML for Model Compression and Acceleration on Mobile Devices.
awesome-meta-learning
A curated list of Meta-Learning resources/papers.
Benchmark_EPS
A Benchmark for Edge-Preserving Image Smoothing.
benchmark_results
visual tracker benchmark results
burst-cvpr-2019
Iterative Residual CNNs for Burst Photography Applications
Deep-Compression-AlexNet
Deep Compression on AlexNet
deep-high-resolution-net.pytorch
The project is an official implement of our CVPR2019 paper "Deep High-Resolution Representation Learning for Human Pose Estimation"
DNI
CVPR19 - Deep Network Interpolation for Continuous Imagery Effect Transition
fritz-style-transfer
Train and deploy real-time artistic style transfer in mobile apps with Fritz Style Transfer.
maskfusion
MaskFusion: Real-Time Recognition, Tracking and Reconstruction of Multiple Moving Objects
ML_GCN
PyTorch implementation of Multi-Label Image Recognition with Graph Convolutional Networks, CVPR 2019.
Person_reID_baseline_pytorch
A tiny, friendly, strong pytorch implement of person re-identification baseline. Tutorial 👉https://github.com/layumi/Person_reID_baseline_pytorch/tree/master/tutorial
pytorch-tutorial
PyTorch Tutorial for Deep Learning Researchers
RandWireNN
Implementation of: "Exploring Randomly Wired Neural Networks for Image Recognition"
Reinforcement-learning-with-tensorflow
Simple Reinforcement learning tutorials
SCIELAB-1996
Initial S-CIELAB Implementation (Zhang and Wandell)
Single-Image-Deraining
Single Image Deraining: A Comprehensive Benchmark Analysis
texture-vs-shape
Pre-trained models, data, code & materials from the paper "ImageNet-trained CNNs are biased towards texture; increasing shape bias improves accuracy and robustness" (ICLR 2019 Oral)
triplet-reid
Code for reproducing the results of our "In Defense of the Triplet Loss for Person Re-Identification" paper.
Variational-Autoencoder-with-Arbitrary-Conditioning
PyTorch implementation (unofficial) of the ICLR 2019 paper 'Variational Autoencoder with Arbitrary Conditioning'