Name: 蔡伟伟
Type: User
Company: Fudan University
Bio: I am Weiwei cai, a student in Fudan University. I want to study in Github and try to make some contributions to the community.
Location: Fudan University
蔡伟伟's Projects
The most popular HTML, CSS, and JavaScript framework for developing responsive, mobile first projects on the web.
因遗留原因,自动更新不好实现,本项目不再维护,新项目地址 https://github.com/Limour-dev/daily_fudan_actions
deep learning for image processing including classification and object-detection etc.
the first update
作业需要提交以下内容: 提交内容 详细要求 作业文档 对方法原理进行简单的说明,对实验结果进行分析。 程序源代码 相关程序的全部源代码,要求能够正常编译和运行。 程序说明 详细说明如何编译源代码、如何运行演示程序。 实验一: 直方图均衡化 作业要求: 1. 参考“空间域图像增强” 和“彩色图像处理”(自我探索)内容, 对灰度和彩色图片进行直方图均衡化处理, 输出均衡化后的图片。 2. 分别在 RGB 颜色空间和 HSI 颜色空间下对彩色图片进行直方图均衡化操作, 输出均衡化后的图片, 观察并分析两个颜色空间下实验结果的差别。 3.实验图片: 灰度图片直方图均衡化: histeq1.jpg、 histeq2.jpg、 histeq3.jpg、 histeq4.jpg。 彩色图片直方图均衡化: histeqColor.jpg 再自选至少 5 张图片(包括灰度图片和彩色图片
A pytorch implementation of dreamfields with modifications.
Using modified BiSeNet for face parsing in PyTorch
Demo code for our CVPR'18 paper "FSRNet: End-to-End Learning Face Super-Resolution with Facial Priors" (SPOTLIGHT Presentation)
This is an official implementation of semantic segmentation for our TPAMI paper "Deep High-Resolution Representation Learning for Visual Recognition". https://arxiv.org/abs/1908.07919
人脸解析比赛
Deep Learning for humans
PyTorch Implementation of the paper Learning to Reweight Examples for Robust Deep Learning
A new code framework that uses pytorch to implement meta-learning, and takes Meta-Weight-Net as an example.
notes about machine learning
git和github演示项目
A PyTorch implementation of NeRF (Neural Radiance Fields) that reproduces the results.
OpenCV-Python图像处理教程
PyTorch implementations of Generative Adversarial Networks.
Single Image Super-Resolution from Transformed Self-Exemplars (CVPR 2015)
Image Super-Resolution by Neural Texture Transfer