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

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.


Contents

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

Owner
Jonathan Choi
Researching Computer Vision @ Korea University. To The Infinity, And Beyond!
Jonathan Choi
Spectralformer: Rethinking hyperspectral image classification with transformers

Spectralformer: Rethinking hyperspectral image classification with transformers Danfeng Hong, Zhu Han, Jing Yao, Lianru Gao, Bing Zhang, Antonio Plaza

Danfeng Hong 102 Dec 29, 2022
The official PyTorch implementation of Curriculum by Smoothing (NeurIPS 2020, Spotlight).

Curriculum by Smoothing (NeurIPS 2020) The official PyTorch implementation of Curriculum by Smoothing (NeurIPS 2020, Spotlight). For any questions reg

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An unopinionated replacement for PyTorch's Dataset and ImageFolder, that handles Tar archives

Simple Tar Dataset An unopinionated replacement for PyTorch's Dataset and ImageFolder classes, for datasets stored as uncompressed Tar archives. Just

Joao Henriques 47 Dec 20, 2022
A very short and easy implementation of Quantile Regression DQN

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Arsenii Senya Ashukha 80 Sep 17, 2022
UFT - Universal File Transfer With Python

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SegTransVAE: Hybrid CNN - Transformer with Regularization for medical image segmentation

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This is a collection of our NAS and Vision Transformer work.

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Microsoft 828 Dec 28, 2022
Implementation of the final project of the course DDA6309 Probabilistic Graphical Model

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Peng 1 Dec 26, 2021
Automatic Data-Regularized Actor-Critic (Auto-DrAC)

Auto-DrAC: Automatic Data-Regularized Actor-Critic This is a PyTorch implementation of the methods proposed in Automatic Data Augmentation for General

89 Dec 13, 2022
Practical tutorials and labs for TensorFlow used by Nvidia, FFN, CNN, RNN, Kaggle, AE

TensorFlow Tutorial - used by Nvidia Learn TensorFlow from scratch by examples and visualizations with interactive jupyter notebooks. Learn to compete

Alexander R Johansen 1.9k Dec 19, 2022
Deep Ensemble Learning with Jet-Like architecture

Ransomware analysis using DEL with jet-like architecture comprising two CNN wings, a sparse AE tail, a non-linear PCA to produce a diverse feature space, and an MLP nose

Ahsen Nazir 2 Feb 06, 2022
CVPR 2021 - Official code repository for the paper: On Self-Contact and Human Pose.

selfcontact This repo is part of our project: On Self-Contact and Human Pose. [Project Page] [Paper] [MPI Project Page] It includes the main function

Lea Müller 68 Dec 06, 2022
PyToch implementation of A Novel Self-supervised Learning Task Designed for Anomaly Segmentation

Self-Supervised Anomaly Segmentation Intorduction This is a PyToch implementation of A Novel Self-supervised Learning Task Designed for Anomaly Segmen

WuFan 2 Jan 27, 2022
exponential adaptive pooling for PyTorch

AdaPool: Exponential Adaptive Pooling for Information-Retaining Downsampling Abstract Pooling layers are essential building blocks of Convolutional Ne

Alexandros Stergiou 55 Jan 04, 2023
Decorators for maximizing memory utilization with PyTorch & CUDA

torch-max-mem This package provides decorators for memory utilization maximization with PyTorch and CUDA by starting with a maximum parameter size and

Max Berrendorf 10 May 02, 2022
Code base for NeurIPS 2021 publication titled Kernel Functional Optimisation (KFO)

KernelFunctionalOptimisation Code base for NeurIPS 2021 publication titled Kernel Functional Optimisation (KFO) We have conducted all our experiments

2 Jun 29, 2022
Official PyTorch implementation of "Contrastive Learning from Extremely Augmented Skeleton Sequences for Self-supervised Action Recognition" in AAAI2022.

AimCLR This is an official PyTorch implementation of "Contrastive Learning from Extremely Augmented Skeleton Sequences for Self-supervised Action Reco

Gty 44 Dec 17, 2022
Learning View Priors for Single-view 3D Reconstruction (CVPR 2019)

Learning View Priors for Single-view 3D Reconstruction (CVPR 2019) This is code for a paper Learning View Priors for Single-view 3D Reconstruction by

Hiroharu Kato 38 Aug 17, 2022
Existing Literature about Machine Unlearning

Machine Unlearning Papers 2021 Brophy and Lowd. Machine Unlearning for Random Forests. In ICML 2021. Bourtoule et al. Machine Unlearning. In IEEE Symp

Jonathan Brophy 213 Jan 08, 2023
Official PyTorch implementation of Retrieve in Style: Unsupervised Facial Feature Transfer and Retrieval.

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