Pytorch implementation of the paper: "SAPNet: Segmentation-Aware Progressive Network for Perceptual Contrastive Image Deraining"

Overview

SAPNet

This repository contains the official Pytorch implementation of the paper: "SAPNet: Segmentation-Aware Progressive Network for Perceptual Contrastive Image Deraining"

Updates:

Code will be updated before 2021/11/23 **Arxiv Link is available at https://arxiv.org/abs/2111.08892

Abstract

Deep learning algorithms have recently achieved promising deraining performances on both the natural and synthetic rainy datasets. As an essential low-level pre-processing stage, a deraining network should clear the rain streaks and preserve the fine semantic details. However, most existing methods only consider low-level image restoration. That limits their performances at high-level tasks requiring precise semantic information. To address this issue, in this paper, we present a segmentation-aware progressive network (SAPNet) based upon contrastive learning for single image deraining. We start our method with a lightweight derain network formed with progressive dilated units (PDU). The PDU can significantly expand the receptive field and characterize multi-scale rain streaks without the heavy computation on multi-scale images. A fundamental aspect of this work is an unsupervised background segmentation (UBS) network initialized with ImageNet and Gaussian weights. The UBS can faithfully preserve an image's semantic information and improve the generalization ability to unseen photos. Furthermore, we introduce a perceptual contrastive loss (PCL) and a learned perceptual image similarity loss (LPISL) to regulate model learning. By exploiting the rainy image and groundtruth as the negative and the positive sample in the VGG-16 latent space, we bridge the fine semantic details between the derained image and the groundtruth in a fully constrained manner. Comprehensive experiments on synthetic and real-world rainy images show our model surpasses top-performing methods and aids object detection and semantic segmentation with considerable efficacy.

Preparing Dataset

First, download training and testing dataset from either link BaiduYun OneDrive

Next, create new folders called dataset. Then create sub-folders called train and test under that folder. Finally, place the unzipped folders into ./datasets/train/ (training data) and ./datasets/test/ (testing data)

Training

Run the following script in terminal

python train.py

Testing

Run the following script in terminal

bash main.sh

Hyperparameters

General Hyperparameters

Name Type Default Description
preprocess bool False
batch_size int 12
epochs int 100
milestone int [30,50,80]
lr float 0.001
save_path str logs/SAPNet/Model11
save_freq int 1

Train/Test Hypeparameters

Name Type Default Description
test_data_path str datasets/test/Rain100H
output_path str results/Rain100H/Model11
data_path str datasets/train/RainTrainH
use_contrast bool True
use_seg_stage1 bool True
use_stage1 bool True
use_dilation bool True
recurrent_iter int 6
num_of_SegClass int 21

Contact

Please reach [email protected] for further questions. You can also open an issue (prefered) or a pull request in this Github repository

Acknowledgement

This repository is borrowed heavily from PreNet. Thanks for sharing!

TODO List

  • Upload Pretrained Weight
  • Add Visual Comparisons
  • Add References
  • Upload Arxiv Link
  • Upload BibTeX
This repository contains codes of ICCV2021 paper: SO-Pose: Exploiting Self-Occlusion for Direct 6D Pose Estimation

SO-Pose This repository contains codes of ICCV2021 paper: SO-Pose: Exploiting Self-Occlusion for Direct 6D Pose Estimation This paper is basically an

shangbuhuan 52 Nov 25, 2022
Create Own QR code with Python

Create-Own-QR-code Create Own QR code with Python SO guys in here, you have to install pyqrcode 2. open CMD and type python -m pip install pyqrcode

JehanKandy 10 Jul 13, 2022
Official PyTorch implementation of GDWCT (CVPR 2019, oral)

This repository provides the official code of GDWCT, and it is written in PyTorch. Paper Image-to-Image Translation via Group-wise Deep Whitening-and-

WonwoongCho 135 Dec 02, 2022
Multiview 3D object detection on MultiviewC dataset through moft3d.

Multiview Orthographic Feature Transformation for 3D Object Detection Multiview 3D object detection on MultiviewC dataset through moft3d. Introduction

Jiahao Ma 20 Dec 21, 2022
[CVPR 2021] Unsupervised Degradation Representation Learning for Blind Super-Resolution

DASR Pytorch implementation of "Unsupervised Degradation Representation Learning for Blind Super-Resolution", CVPR 2021 [arXiv] Overview Requirements

Longguang Wang 318 Dec 24, 2022
[Preprint] ConvMLP: Hierarchical Convolutional MLPs for Vision, 2021

Convolutional MLP ConvMLP: Hierarchical Convolutional MLPs for Vision Preprint link: ConvMLP: Hierarchical Convolutional MLPs for Vision By Jiachen Li

SHI Lab 143 Jan 03, 2023
RodoSol-ALPR Dataset

RodoSol-ALPR Dataset This dataset, called RodoSol-ALPR dataset, contains 20,000 images captured by static cameras located at pay tolls owned by the Ro

Rayson Laroca 45 Dec 15, 2022
PyTorch implementation of "Dataset Knowledge Transfer for Class-Incremental Learning Without Memory" (WACV2022)

Dataset Knowledge Transfer for Class-Incremental Learning Without Memory [Paper] [Slides] Summary Introduction Installation Reproducing results Citati

Habib Slim 5 Dec 05, 2022
Logsig-RNN: a novel network for robust and efficient skeleton-based action recognition

GCN_LogsigRNN This repository holds the codebase for the paper: Logsig-RNN: a novel network for robust and efficient skeleton-based action recognition

7 Oct 14, 2022
Tackling the Class Imbalance Problem of Deep Learning Based Head and Neck Organ Segmentation

Info This is the code repository of the work Tackling the Class Imbalance Problem of Deep Learning Based Head and Neck Organ Segmentation from Elias T

2 Apr 20, 2022
This python-based package offers a way of creating a parametric OpenMC plasma source from plasma parameters.

openmc-plasma-source This python-based package offers a way of creating a parametric OpenMC plasma source from plasma parameters. The OpenMC sources a

Fusion Energy 10 Oct 18, 2022
Deep deconfounded recommender (Deep-Deconf) for paper "Deep causal reasoning for recommendations"

Deep Causal Reasoning for Recommender Systems The codes are associated with the following paper: Deep Causal Reasoning for Recommendations, Yaochen Zh

Yaochen Zhu 22 Oct 15, 2022
Learning Open-World Object Proposals without Learning to Classify

Learning Open-World Object Proposals without Learning to Classify Pytorch implementation for "Learning Open-World Object Proposals without Learning to

Dahun Kim 149 Dec 22, 2022
[ICCV'2021] Image Inpainting via Conditional Texture and Structure Dual Generation

[ICCV'2021] Image Inpainting via Conditional Texture and Structure Dual Generation

Xiefan Guo 122 Dec 11, 2022
Deep Residual Networks with 1K Layers

Deep Residual Networks with 1K Layers By Kaiming He, Xiangyu Zhang, Shaoqing Ren, Jian Sun. Microsoft Research Asia (MSRA). Table of Contents Introduc

Kaiming He 856 Jan 06, 2023
Enhancing Aspect-Based Sentiment Analysis with Supervised Contrastive Learning.

Enhancing Aspect-Based Sentiment Analysis with Supervised Contrastive Learning. Enhancing Aspect-Based Sentiment Analysis with Supervised Contrastive

<a href=[email protected](SZ)"> 7 Dec 16, 2021
Attention Probe: Vision Transformer Distillation in the Wild

Attention Probe: Vision Transformer Distillation in the Wild Jiahao Wang, Mingdeng Cao, Shuwei Shi, Baoyuan Wu, Yujiu Yang In ICASSP 2022 This code is

Wang jiahao 3 Oct 31, 2022
Parameter Efficient Deep Probabilistic Forecasting

PEDPF Parameter Efficient Deep Probabilistic Forecasting (PEDPF) is a repository containing code to run experiments for several deep learning based pr

Olivier Sprangers 10 Jun 13, 2022
Unicorn can be used for performance analyses of highly configurable systems with causal reasoning

Unicorn can be used for performance analyses of highly configurable systems with causal reasoning. Users or developers can query Unicorn for a performance task.

AISys Lab 27 Jan 05, 2023
Code for our EMNLP 2021 paper “Heterogeneous Graph Neural Networks for Keyphrase Generation”

GATER This repository contains the code for our EMNLP 2021 paper “Heterogeneous Graph Neural Networks for Keyphrase Generation”. Our implementation is

Jiacheng Ye 12 Nov 24, 2022