(AAAI 2021) Progressive One-shot Human Parsing

Related tags

Deep LearningOne-shot-Human-Parsing
Overview

End-to-end One-shot Human Parsing

This is the official repository for our two papers:

Introduction:

In the two papers, we propose a new task named One-shot Human Parsing (OSHP). OSHP requires parsing humans in a query image into an open set of reference classes defined by any single reference example (i.e., a support image) during testing, no matter whether they have been annotated during training (base classes) or not (novel classes). This new task mainly aims to accommodate human parsing into a wider range of applications that seek to parse flexible fashion/clothing classes that are not pre-defined in previous large-scale datasets.

Progressive One-shot Human Parsing (AAAI 2021) applies a progressive training scheme and is separated into three stages.

End-to-end One-shot Human Parsing (journal version) is a one-stage end-to-end training method, which has higher performance and FPS.

Main results:

You can find the well-trained models together with the performance in the following table.

EOPNet ATR-OS, Kway F1 ATR-OS, Kway Fold F2 LIP-OS, Kway F1 LIP-OS, Kway F2 CIHP-OS, Kway F1 CIHP-OS Kway F2
Novel mIoU 31.1 34.6 25.7 30.4 20.5 25.1
Human mIoU 61.9 63.3 43.0 45.7 49.1 45.5
Model Model Coming Soon Model Model Model Model

You can find the well-trained models together with the performance in the following table.

EOPNet ATR-OS, 1way F1 ATR-OS, 1way F2 LIP-OS, 1way F1 LIP-OS, 1way F2 CIHP-OS, 1way F1 CIHP-OS 1way F2
Novel mIoU 53.0 41.4 42.0 46.2 25.4 36.4
Human mIoU 68.2 69.5 57.0 58.0 53.8 55.4
Model Coming Soon

Getting started:

Data preparation:

First, please download ATR, LIP and CIHP dataset from source. Then, use the following commands to link the data into our project folder. Please also remember to download the atr flipped labels and cihp flipped labels.

# ATR dataset
$ ln -s YOUR_ATR_PATH/JPEGImages/* YOUR_PROJECT_ROOT/ATR_OS/trainval_images
$ ln -s YOUR_ATR_PATH/SegmentationClassAug/* YOUR_PROJECT_ROOT/ATR_OS/trainval_classes
$ ln -s YOUR_ATR_PATH/SegmentationClassAug_rev/* YOUR_PROJECT_ROOT/ATR_OS/Category_rev_ids


# LIP dataset
$ ln -s YOUR_LIP_PATH/TrainVal_images/TrainVal_images/train_images/* YOUR_PROJECT_ROOT/LIP_OS/trainval_images
$ ln -s YOUR_LIP_PATH/TrainVal_images/TrainVal_images/val_images/* YOUR_PROJECT_ROOT/LIP_OS/trainval_images
$ ln -s YOUR_LIP_PATH/TrainVal_parsing_annotations/TrainVal_parsing_annotations/train_segmentations/* YOUR_PROJECT_ROOT/LIP_OS/trainval_classes
$ ln -s YOUR_LIP_PATH/TrainVal_parsing_annotations/TrainVal_parsing_annotations/val_segmentations/* YOUR_PROJECT_ROOT/LIP_OS/trainval_classes
$ ln -s YOUR_LIP_PATH/Train_parsing_reversed_labels/TrainVal_parsing_annotations/* YOUR_PROJECT_ROOT/LIP_OS/Category_rev_ids
$ ln -s YOUR_LIP_PATH/val_segmentations_reversed/* YOUR_PROJECT_ROOT/LIP_OS/Category_rev_ids


# CIHP dataset
$ ln -s YOUR_CIHP_PATH/Training/Images/* YOUR_PROJECT_ROOT/CIHP_OS/trainval_images
$ ln -s YOUR_CIHP_PATH/Validation/Images/* YOUR_PROJECT_ROOT/CIHP_OS/trainval_images
$ ln -s YOUR_CIHP_PATH/Training/Category_ids/* YOUR_PROJECT_ROOT/CIHP_OS/trainval_classes
$ ln -s YOUR_CIHP_PATH/Validation/Category_ids/* YOUR_PROJECT_ROOT/CIHP_OS/trainval_classes
$ ln -s YOUR_CIHP_PATH/Category_rev_ids/* YOUR_PROJECT_ROOT/CIHP_OS/Category_rev_ids

Please also download our generated support .pkl files from source, which contains each class's image IDs. You can also generate support files on your own by controlling dtrain_dtest_split in oshp_loader.py, however, the training and validation list might be different from our paper.

Finally, your data folder should look like this:

${PROJECT ROOT}
|-- data
|   |--datasets
|       |-- ATR_OS
|       |   |-- list
|       |   |   |-- meta_train_id.txt
|       |   |   `-- meta_test_id.txt
|       |   |-- support
|       |   |   |-- meta_train_atr_supports.pkl
|       |   |   `-- meta_test_atr_supports.pkl
|       |   |-- trainval_images
|       |   |   |-- 997-1.jpg
|       |   |   |-- 997-2.jpg
|       |   |   `-- ...
|       |   |-- trainval_classes
|       |   |   |-- 997-1.png
|       |   |   |-- 997-2.png
|       |   |   `-- ... 
|       |   `-- Category_rev_ids
|       |       |-- 997-1.png
|       |       |-- 997-2.png
|       |       `-- ... 
|       |-- LIP_OS
|       |   |-- list
|       |   |   |-- meta_train_id.txt
|       |   |   |-- meta_test_id.txt
|       |   |-- support
|       |   |   |-- meta_train_lip_supports.pkl
|       |   |   `-- meta_test_lip_supports.pkl
|       |   |-- trainval_images
|       |   |   |-- ...
|       |   |-- trainval_classes
|       |   |   |-- ... 
|       |   `-- Category_rev_ids
|       |       |-- ... 
|       `-- CIHP_OS
|           |-- list
|           |   |-- meta_train_id.txt
|           |   |-- meta_test_id.txt
|           |-- support
|           |   |-- meta_train_cihp_supports.pkl
|           |   `-- meta_test_cihp_supports.pkl
|           |-- trainval_images
|           |   |-- ...
|           |-- trainval_classes
|           |   |-- ... 
|           `-- Category_rev_ids
|               |-- ...

Finally, please download the DeepLab V3+ pretrained model (pretrained on COCO dataset) from source and put it into the data folder:

${PROJECT ROOT}
|-- data
|   |--pretrained_model
|       |--deeplab_v3plus_v3.pth

Installation:

Please make sure your current environment has Python >= 3.7.0 and pytorch >= 1.1.0. The pytorch can be downloaded from source.

Then, clone the repository and install the dependencies from the following commands:

git clone https://github.com/Charleshhy/One-shot-Human-Parsing.git
cd One-shot-Human-Parsing
pip install -r requirements.txt

Training:

To train EOPNet in End-to-end One-shot Human Parsing (journal version), run:

# OSHP kway on ATR-OS fold 1
bash scripts/atr_eop_kwf1.sh

Validation:

To evaluate EOPNet in End-to-end One-shot Human Parsing (journal version), run:

# OSHP kway on ATR-OS fold 1
bash scripts/evaluate_atr_eop_kwf1.sh

TODO:

  • Release training/validation code for POPNet
  • Release well-trained EOPNet 1-way models

Citation:

If you find our papers or this repository useful, please consider cite our papers:

@inproceedings{he2021progressive,
title={Progressive One-shot Human Parsing},
author={He, Haoyu and Zhang, Jing and Thuraisingham, Bhavani and Tao, Dacheng},
booktitle={Proceedings of the AAAI Conference on Artificial Intelligence},
year={2021}
}

@article{he2021end,
title={End-to-end One-shot Human Parsing},
author={He, Haoyu and Zhang, Jing and Zhuang, Bohan and Cai, Jianfei and Tao, Dacheng},
journal={arXiv preprint arXiv:2105.01241},
year={2021}
}

Acknowledgement:

This repository is mainly developed basing on Graphonomy and Grapy-ML.

Owner
GitHub Repository
atmaCup #11 の Public 4th / Pricvate 5th Solution のリポジトリです。

#11 atmaCup 2021-07-09 ~ 2020-07-21 に行われた #11 [初心者歓迎! / 画像編] atmaCup のリポジトリです。結果は Public 4th / Private 5th でした。 フレームワークは PyTorch で、実装は pytorch-image-m

Tawara 12 Apr 07, 2022
A tensorflow implementation of GCN-LPA

GCN-LPA This repository is the implementation of GCN-LPA (arXiv): Unifying Graph Convolutional Neural Networks and Label Propagation Hongwei Wang, Jur

Hongwei Wang 83 Nov 28, 2022
official implementation for the paper "Simplifying Graph Convolutional Networks"

Simplifying Graph Convolutional Networks Updates As pointed out by #23, there was a subtle bug in our preprocessing code for the reddit dataset. After

Tianyi 727 Jan 01, 2023
Annotate datasets with a semi-trained or fully trained YOLOv5 model

YOLOv5 Auto Annotator Annotate datasets with a semi-trained or fully trained YOLOv5 model Prerequisites Ubuntu =20.04 Python =3.7 System dependencie

Akash James 3 May 14, 2022
The 2nd Version Of Slothybot

SlothyBot Go to this website: "https://bitly.com/SlothyBot" The 2nd Version Of Slothybot. The Bot Has Many Features, Such As: Moderation Commands; Kic

Slothy 0 Jun 01, 2022
PPLNN is a Primitive Library for Neural Network is a high-performance deep-learning inference engine for efficient AI inferencing

PPLNN is a Primitive Library for Neural Network is a high-performance deep-learning inference engine for efficient AI inferencing

943 Jan 07, 2023
Code for Multiple Instance Active Learning for Object Detection, CVPR 2021

MI-AOD Language: 简体中文 | English Introduction This is the code for Multiple Instance Active Learning for Object Detection (The PDF is not available tem

Tianning Yuan 269 Dec 21, 2022
Incremental Cross-Domain Adaptation for Robust Retinopathy Screening via Bayesian Deep Learning

Incremental Cross-Domain Adaptation for Robust Retinopathy Screening via Bayesian Deep Learning Update (September 18th, 2021) A supporting document de

Taimur Hassan 1 Mar 16, 2022
SeisComP/SeisBench interface to enable deep-learning (re)picking in SeisComP

scdlpicker SeisComP/SeisBench interface to enable deep-learning (re)picking in SeisComP Objective This is a simple deep learning (DL) repicker module

Joachim Saul 6 May 13, 2022
MPRNet-Cloud-removal: Progressive cloud removal

MPRNet-Cloud-removal Progressive cloud removal Requirements 1.Pytorch = 1.0 2.Python 3 3.NVIDIA GPU + CUDA 9.0 4.Tensorboard Installation 1.Clone the

Semi 95 Dec 18, 2022
This is the official PyTorch implementation of our paper: "Artistic Style Transfer with Internal-external Learning and Contrastive Learning".

Artistic Style Transfer with Internal-external Learning and Contrastive Learning This is the official PyTorch implementation of our paper: "Artistic S

51 Dec 20, 2022
Preprocessed Datasets for our Multimodal NER paper

Unified Multimodal Transformer (UMT) for Multimodal Named Entity Recognition (MNER) Two MNER Datasets and Codes for our ACL'2020 paper: Improving Mult

76 Dec 21, 2022
Fast methods to work with hydro- and topography data in pure Python.

PyFlwDir Intro PyFlwDir contains a series of methods to work with gridded DEM and flow direction datasets, which are key to many workflows in many ear

Deltares 27 Dec 07, 2022
Just-Now - This Is Just Now Login Friendlist Cloner Tools

JUST NOW LOGIN FRIENDLIST CLONER TOOLS Install $ apt update $ apt upgrade $ apt

MAHADI HASAN AFRIDI 21 Mar 09, 2022
Learnable Boundary Guided Adversarial Training (ICCV2021)

Learnable Boundary Guided Adversarial Training This repository contains the implementation code for the ICCV2021 paper: Learnable Boundary Guided Adve

DV Lab 27 Sep 25, 2022
OpenGAN: Open-Set Recognition via Open Data Generation

OpenGAN: Open-Set Recognition via Open Data Generation ICCV 2021 (oral) Real-world machine learning systems need to analyze novel testing data that di

Shu Kong 90 Jan 06, 2023
Codes for AAAI 2022 paper: Context-aware Health Event Prediction via Transition Functions on Dynamic Disease Graphs

Context-Aware-Healthcare Codes for AAAI 2022 paper: Context-aware Health Event Prediction via Transition Functions on Dynamic Disease Graphs Download

LuChang 9 Dec 26, 2022
VIMPAC: Video Pre-Training via Masked Token Prediction and Contrastive Learning

This is a release of our VIMPAC paper to illustrate the implementations. The pretrained checkpoints and scripts will be soon open-sourced in HuggingFace transformers.

Hao Tan 74 Dec 03, 2022
10x faster matrix and vector operations

Bolt is an algorithm for compressing vectors of real-valued data and running mathematical operations directly on the compressed representations. If yo

2.3k Jan 09, 2023
Translate darknet to tensorflow. Load trained weights, retrain/fine-tune using tensorflow, export constant graph def to mobile devices

Intro Real-time object detection and classification. Paper: version 1, version 2. Read more about YOLO (in darknet) and download weight files here. In

Trieu 6.1k Dec 30, 2022