Name: Min Sheng Wu
Type: User
Company: @aetherAI
Bio: Hello, I am Min-Sheng Wu, or called me Vincent. My interest is in the research about the technology of ML, DL, AI in the applications of Computer Vision (CV).
Location: Taipei, Taiwan
Blog: https://Min-Sheng.github.io
Min Sheng Wu's Projects
Evaluation of Deep Learning Strategies for Nucleus Segmentation in Fluorescence Images
A SlidingWindow-Free Accurate and Fast 3D Medical Image Segmentation Framework
Keras 3D U-Net Convolution Neural Network (CNN) designed for medical image segmentation
Public code for our submission to the 2017 ACDC Cardiac Segmentation challenge
implementation of the ADCrowdNet in pytorch
fast image augmentation library and easy to use wrapper around other libraries
A simple PyTorch Implementation of Generative Adversarial Networks, focusing on anime face drawing.
A project to attempt to automatically login to a website given a single seed
🪐 A curated list of awesome Blender addons, tools, tutorials; and 3D resources for everyone.
A curated list of resources for Learning with Noisy Labels
A curated list of awesome Python frameworks, libraries, software and resources
Promise based HTTP client for the browser and node.js
CVPR 2020 oral paper: Overcoming Classifier Imbalance for Long-tail Object Detection with Balanced Group Softmax.
Make bilingual epub books Using AI translate
My blog:
A Python-Based Blur Detector using Fast Fourier Transforms
Implementation of "Data augmentation using learned transforms for one-shot medical image segmentation"
An open-source PyTorch code for crowd counting
Implementation of "Class-agnostic Few-shot Instance Segmentation of Digital Pathological Images" in Pytorch
The code for paper "CANet: Class-Agnostic Segmentation Networks with Iterative Refinement and Attentive Few-Shot Learning"
Pytorch and Torch testing code of CartoonGAN [Chen et al., CVPR18]
a generalist algorithm for cellular segmentation
Online Demo for Class-agnostic Few-shot Instance Segmentation of Pathological Images
Complete-IoU (CIoU) Loss and Cluster-NMS for Object Detection and Instance Segmentation (YOLACT)
Pytorch implementation of the paper "Class-Balanced Loss Based on Effective Number of Samples"
COCO API - Dataset @ http://cocodataset.org/
A complete computer science study plan to become a software engineer.