Computer Vision Paper Reviews with Key Summary of paper, End to End Code Practice and Jupyter Notebook converted papers

Jonathan Choi

Last update: Mar 17, 2022

Related tags

Deep Learning computer-vision deep-learning transformer image-generation segmentation convolutional-neural-networks object-detection papers representation-learning paper-implementations paper-reviews

Overview

Computer-Vision-Paper-Reviews

Computer Vision Paper Reviews with Key Summary along Papers & Codes.

Jonathan Choi 2021

The repository provides 100+ Papers across Computer Vision fields converted as Jupyter Notebook, with the Key Summary and End to End Code Practice.

The goal of the repository is providing an end to end study scripts of most read and important papers.

The prefered readers are not limited for researchers, but also for students and engieeners from rookies to the professions in computer vision fields .

To provide the perfect and rich understanding, each paper contains following three main contents.

Key Summary

Providing key summaries and terminologies of the paper so that even rookies can study as perfectly and easily as possible.

Code Practice

Providing an end to end study script of codes for the paper so that even rookies can study as easily and perfectly as possible.

Jupyter Notebook edited Original Paper

Providing the Original Paper converted into Jupyter notbook for easy and fast modification and understanding.

Category/Paper/

Paper_Review_Practice.ipynb includes

Key Summary according to the flow of Original Paper (Jupyter Notebook Edited) with the End to End Code Practice

Paper.ipynb includes

Original Paper (Jupyter Notebook Edited)

Review.ipynb includes

Key Summary

Practice.ipynb includes

End to End Code Practice

Index

Working Papers

If you want to see Road Map and the process, please visit here.

Implicit Neural Representation

Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift

Instance Normalization: The Missing Ingredient for Fast Stylization

Arbitrary Style Transfer in Real-time with Adaptive Instance Normalization

Semantic Image Synthesis with Spatially-Adaptive Normalization

Universal Style Transfer via Feature Transforms

A Neural Algorithm of Artistic Style

Convolutional neural network architecture for geometric matching

Perceptual Losses for Real-Time Style Transfer and Super-Resolution

Geometric Style Transfer

Image to Image Translation

Image-to-Image Translation with Conditional Adversarial Networks (CVPR 2017)

Bi-level Feature Alignment for Versatile Image Translation and Manipulation

Transformer

[DETR] End-to-End Object Detection with Transformers

[Vision Transformer] An Image Is Worth 16x16 Words: Transformers For Image Recognition at Scale

Swin Transformer: Hierarchical Vision Transformer using Shifted Windows

[Transformer] Attention Is All You Need

Vision Transformers for Dense Prediction

Object Detection

Feature Pyramid Networks for Object Detection

Selective Search for Object Recognition

R-CNN

Fast R-CNN

Faster R-CNN

Sparse R-CNN

YOLOv4: Optimal Speed and Accuracy of Object Detection**

Segmentation

Panoptic Feature Pyramid Networks

Mask R-CNN

PointRend: Image Segmentation as Rendering

Cost Aggregation Is All You Need for Few-Shot Segmentation

Convolutional Neural Network

Deep Residual Learning for Image Recognition

EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks

Representation Learning

Unsupervised Feature Learning via Non-Parametric Instance Discrimination

Momentum Contrast for Unsupervised Visual Representation Learning.

A Simple Framework for Contrastive Learning of Visual Representations

Bootstrap Your Own Latent- A New Approach to Self-supervised Learning

Exploring Simple Siamese Representation Learning

Image Generation

Generative Adversarial Networks

A Style-Based Generator Architecture for Generative Adversarial Networks

GAN Dissection: Visualizing and Understanding Generative Adversarial Networks

Semantic Image Synthesis with Spatially-Adaptive Normalization

Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks

Vision and Language

Depth Estimation

Correspondence

Implicit Field

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