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Hi 👋, I'm Yuzhi @zhaoyuzhi

Researcher in Computer Vision | Ph.D., City University of Hong Kong

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Yuzhi ZHAO's Projects

pytorch-vae icon pytorch-vae

A Collection of Variational Autoencoders (VAE) in PyTorch.

pytorch_wavelets icon pytorch_wavelets

Pytorch implementation of 2D Discrete Wavelet (DWT) and Dual Tree Complex Wavelet Transforms (DTCWT)

qrnet icon qrnet

source code of QRNet (Joint Image Deblurring and Denoising with Dual-exposure Quad-Bayer Sensors), which is submitted to IEEE Transactions on Image Processing

raw2rgb-gan icon raw2rgb-gan

PyTorch implementation of saliency map-aided GAN for Auto-demosaic+denosing

self-attention-gan icon self-attention-gan

Pytorch implementation of Self-Attention Generative Adversarial Networks (SAGAN)

sidd-benchmark icon sidd-benchmark

Users can freely try their models on the SIDD dataset based on this code

stylegan2-pytorch icon stylegan2-pytorch

Implementation of Analyzing and Improving the Image Quality of StyleGAN (StyleGAN 2) in PyTorch

super-slomo icon super-slomo

PyTorch implementation of Super SloMo by Jiang et al.

svcnet icon svcnet

supplementary material for paper "SVCNet: Real-time Scribble-based Video Colorization with Pyramid Networks", under review of IEEE Transactions on Image Processing

swinir icon swinir

SwinIR: Image Restoration Using Swin Transformer

transunet icon transunet

This repository includes the official project of TransUNet, presented in our paper: TransUNet: Transformers Make Strong Encoders for Medical Image Segmentation.

vcgan icon vcgan

VCGAN: Video Colorization with Hybrid Generative Adversarial Network. IEEE TMM, 2022

vit-pytorch icon vit-pytorch

Implementation of Vision Transformer, a simple way to achieve SOTA in vision classification with only a single transformer encoder, in Pytorch

youtube-dl icon youtube-dl

Command-line program to download videos from YouTube.com and other video sites

zhaoyuzhi.github.io icon zhaoyuzhi.github.io

Github Pages template for academic personal websites, forked from mmistakes/minimal-mistakes

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