Stars
My solutions to CS106B: Programming Abstractions
下方是我的个人软件测试学习笔记,为了整理与校对这些笔记,我花费了很长时间,毕竟 “追求完美” 是每一位软件测试工程师都应当具备的基本态度。笔记中的图片绝大多数由我本人亲自绘制,我希望这些笔记可以为 “以软件测试工作为职业路径” 的朋友们提供一些学习方面的帮助。
Collection of popular and reproducible image denoising works.
SALT (iccv2017) based Video Denoising Codes, Matlab implementation
Official PyTorch implementation of the paper: “Lightweight Image Super-Resolution by Multi-scale Aggregation”.
Collection of recent shadow removal works, including papers, codes, datasets, and metrics.
ECCV 2022: Learning Shadow Correspondence for Video Shadow Detection
An ultimately comprehensive paper list of Vision Transformer/Attention, including papers, codes, and related websites
Pytorch implementation of "Multi-Stage Edge-Guided Stereo Feature Interaction Network for Stereoscopic Image Super-Resolution"
Code for the ECCV 2018 paper "Bidirectional Feature Pyramid Network with Recurrent Attention Residual Modules for Shadow Detection"
[AAAI 2020] Towards Ghost-free Shadow Removal via Dual Hierarchical Aggregation Network and Shadow Matting GAN
Official PyTorch implementation of the paper: “Style-Guided Shadow Removal” (ECCV2022).
A simple C++ library for introductory CS. Forked from StanfordCPPLib, originally used in Stanford CS106B.
Stanford C++ library used in CS106B/X courses
Dataset and Code for our CVPR'18 paper ST-CGAN: "Stacked Conditional Generative Adversarial Networks for Jointly Learning Shadow Detection and Shadow Removal"
Using the publicly available SBU Dataset, a training pipeline for semantic shadow segmentation . This can be used for identifying and removing shadow regions from images for image quality improvement.
Code for our ICCV 2021 paper "Mitigating Intensity Bias in Shadow Detection via Feature Decomposition and Reweighting"
ShadowFormer (AAAI2023), Pytorch implementation
The best terminal you could have (well for me it is), is very similar to that of VScode.
Neovim plugin.The best code runner you could have, it is like the one in vscode but with super powers, it manages projects like in intellij but without being slow