Adversarial Graph Representation Adaptation for Cross-Domain Facial Expression Recognition (AGRA, ACM 2020, Oral)

Overview

Cross Domain Facial Expression Recognition Benchmark

Implementation of papers:

Pipeline

Environment

Ubuntu 16.04 LTS, Python 3.5, PyTorch 1.3

Note: We also provide docker image for this project, click here. (Tag: py3-pytorch1.3-agra)

Datasets

To apply for the AFE, please complete the AFE Database User Agreement and submit it to [email protected] or [email protected].

Note:

  1. The AFE Database Agreement needs to be signed by the faculty member at a university or college and sent it by email.
  2. In order to comply with relevant regulations, you need to apply for the image data of the following data sets by yourself, including CK+, JAFFE, SFEW 2.0, FER2013, ExpW, RAF.

Pre-Train Model

You can download pre-train models in Baidu Drive (password: tzrf) and OneDrive.

Note: To replace backbone of each methods, you should modify and run getPreTrainedModel_ResNet.py (or getPreTrainedModel_MobileNet.py) in the folder where you want to use the method.

Usage

Before run these script files, you should download datasets and pre-train model, and run getPreTrainedModel_ResNet.py (or getPreTrainedModel_MobileNet.py).

Run ICID

cd ICID
bash Train.sh

Run DFA

cd DFA
bash Train.sh

Run LPL

cd LPL
bash Train.sh

Run DETN

cd DETN
bash TrainOnSourceDomain.sh     # Train Model On Source Domain
bash TransferToTargetDomain.sh  # Then, Transfer Model to Target Domain

Run FTDNN

cd FTDNN
bash Train.sh

Run ECAN

cd ECAN
bash TrainOnSourceDomain.sh     # Train Model On Source Domain
bash TransferToTargetDomain.sh  # Then, Transfer Model to Target Domain

Run CADA

cd CADA
bash TrainOnSourceDomain.sh     # Train Model On Source Domain
bash TransferToTargetDomain.sh  # Then, Transfer Model to Target Domain

Run SAFN

cd SAFN
bash TrainWithSAFN.sh

Run SWD

cd SWD
bash Train.sh

Run AGRA

cd AGRA
bash TrainOnSourceDomain.sh     # Train Model On Source Domain
bash TransferToTargetDomain.sh  # Then, Transfer Model to Target Domain

Result

Souce Domain: RAF

Methods Backbone CK+ JAFFE SFEW2.0 FER2013 ExpW Mean
ICID ResNet-50 74.42 50.70 48.85 53.70 69.54 59.44
DFA ResNet-50 64.26 44.44 43.07 45.79 56.86 50.88
LPL ResNet-50 74.42 53.05 48.85 55.89 66.90 59.82
DETN ResNet-50 78.22 55.89 49.40 52.29 47.58 56.68
FTDNN ResNet-50 79.07 52.11 47.48 55.98 67.72 60.47
ECAN ResNet-50 79.77 57.28 52.29 56.46 47.37 58.63
CADA ResNet-50 72.09 52.11 53.44 57.61 63.15 59.68
SAFN ResNet-50 75.97 61.03 52.98 55.64 64.91 62.11
SWD ResNet-50 75.19 54.93 52.06 55.84 68.35 61.27
Ours ResNet-50 85.27 61.50 56.43 58.95 68.50 66.13

Methods Backbone CK+ JAFFE SFEW2.0 FER2013 ExpW Mean
ICID ResNet-18 67.44 48.83 47.02 53.00 68.52 56.96
DFA ResNet-18 54.26 42.25 38.30 47.88 47.42 46.02
LPL ResNet-18 72.87 53.99 49.31 53.61 68.35 59.63
DETN ResNet-18 64.19 52.11 42.25 42.01 43.92 48.90
FTDNN ResNet-18 76.74 50.23 49.54 53.28 68.08 59.57
ECAN ResNet-18 66.51 52.11 48.21 50.76 48.73 53.26
CADA ResNet-18 73.64 55.40 52.29 54.71 63.74 59.96
SAFN ResNet-18 68.99 49.30 50.46 53.31 68.32 58.08
SWD ResNet-18 72.09 53.52 49.31 53.70 65.85 58.89
Ours ResNet-18 77.52 61.03 52.75 54.94 69.70 63.19

Methods Backbone CK+ JAFFE SFEW2.0 FER2013 ExpW Mean
ICID MobileNet V2 57.36 37.56 38.30 44.47 60.64 47.67
DFA MobileNet V2 41.86 35.21 29.36 42.36 43.66 38.49
LPL MobileNet V2 59.69 40.38 40.14 50.13 62.26 50.52
DETN MobileNet V2 53.49 40.38 35.09 45.88 45.26 44.02
FTDNN MobileNet V2 71.32 46.01 45.41 49.96 62.87 55.11
ECAN MobileNet V2 53.49 43.08 35.09 45.77 45.09 44.50
CADA MobileNet V2 62.79 53.05 43.12 49.34 59.40 53.54
SAFN MobileNet V2 66.67 45.07 40.14 49.90 61.40 52.64
SWD MobileNet V2 68.22 55.40 43.58 50.30 60.04 55.51
Ours MobileNet V2 72.87 55.40 45.64 51.05 63.94 57.78

Souce Domain: AFE

Methods Backbone CK+ JAFFE SFEW2.0 FER2013 ExpW Mean
ICID ResNet-50 56.59 57.28 44.27 46.92 52.91 51.59
DFA ResNet-50 51.86 52.70 38.03 41.93 60.12 48.93
LPL ResNet-50 73.64 61.03 49.77 49.54 55.26 57.85
DETN ResNet-50 56.27 52.11 44.72 42.17 59.80 51.01
FTDNN ResNet-50 61.24 57.75 47.25 46.36 52.89 53.10
ECAN ResNet-50 58.14 56.91 46.33 46.30 61.44 53.82
CADA ResNet-50 72.09 49.77 50.92 50.32 61.70 56.96
SAFN ResNet-50 73.64 64.79 49.08 48.89 55.69 58.42
SWD ResNet-50 72.09 61.50 48.85 48.83 56.22 57.50
Ours ResNet-50 78.57 65.43 51.18 51.31 62.71 61.84

Methods Backbone CK+ JAFFE SFEW2.0 FER2013 ExpW Mean
ICID ResNet-18 54.26 51.17 47.48 46.44 54.85 50.84
DFA ResNet-18 35.66 45.82 34.63 36.88 62.53 43.10
LPL ResNet-18 67.44 62.91 48.39 49.82 54.51 56.61
DETN ResNet-18 44.19 47.23 45.46 45.39 58.41 48.14
FTDNN ResNet-18 58.91 59.15 47.02 48.58 55.29 53.79
ECAN ResNet-18 44.19 60.56 43.26 46.15 62.52 51.34
CADA ResNet-18 72.09 53.99 48.39 48.61 58.50 56.32
SAFN ResNet-18 68.22 61.50 50.46 50.07 55.17 57.08
SWD ResNet-18 77.52 59.15 50.69 51.84 56.56 59.15
Ours ResNet-18 79.84 61.03 51.15 51.95 65.03 61.80

Methods Backbone CK+ JAFFE SFEW2.0 FER2013 ExpW Mean
ICID MobileNet V2 55.04 42.72 34.86 39.94 44.34 43.38
DFA MobileNet V2 44.19 27.70 31.88 35.95 61.55 40.25
LPL MobileNet V2 69.77 50.23 43.35 45.57 51.63 52.11
DETN MobileNet V2 57.36 54.46 32.80 44.11 64.36 50.62
FTDNN MobileNet V2 65.12 46.01 46.10 46.69 53.02 51.39
ECAN MobileNet V2 71.32 56.40 37.61 45.34 64.00 54.93
CADA MobileNet V2 70.54 45.07 40.14 46.72 54.93 51.48
SAFN MobileNet V2 62.79 53.99 42.66 46.61 52.65 51.74
SWD MobileNet V2 64.34 53.52 44.72 50.24 55.85 53.73
Ours MobileNet V2 75.19 54.46 47.25 47.88 61.10 57.18

Mean of All Methods

Souce Domain: RAF

Backbone CK+ JAFFE SFEW2.0 FER2013 ExpW Mean
ResNet-50 75.87 54.30 54.49 54.82 62.09 59.51
ResNet-18 69.43 51.88 47.94 51.72 61.26 56.45
MobileNet V2 60.78 45.15 39.59 47.92 56.46 49.98

Souce Domain: AFE

Backbone CK+ JAFFE SFEW2.0 FER2013 ExpW Mean
ResNet-50 65.41 57.93 47.04 47.26 57.87 55.10
ResNet-18 60.23 56.25 46.95 47.57 58.34 53.87
MobileNet V2 63.57 48.46 40.14 44.91 56.34 50.68

Citation

@article{chen2020cross,
  title={Cross-Domain Facial Expression Recognition: A Unified Evaluation Benchmark and Adversarial Graph Learning},
  author={Chen, Tianshui and Pu, Tao and Wu, Hefeng and Xie, Yuan and Liu, Lingbo and Lin, Liang},
  journal={IEEE Transactions on Pattern Analysis and Machine Intelligence},
  year={2021},
  pages={1-1},
  doi={10.1109/TPAMI.2021.3131222}
}

@inproceedings{xie2020adversarial,
  title={Adversarial Graph Representation Adaptation for Cross-Domain Facial Expression Recognition},
  author={Xie, Yuan and Chen, Tianshui and Pu, Tao and Wu, Hefeng and Lin, Liang},
  booktitle={Proceedings of the 28th ACM international conference on Multimedia},
  year={2020}
}

Contributors

For any questions, feel free to open an issue or contact us:

CLIP2Video: Mastering Video-Text Retrieval via Image CLIP

CLIP2Video: Mastering Video-Text Retrieval via Image CLIP The implementation of paper CLIP2Video: Mastering Video-Text Retrieval via Image CLIP. CLIP2

168 Dec 29, 2022
U-Net Brain Tumor Segmentation

U-Net Brain Tumor Segmentation 🚀 :Feb 2019 the data processing implementation in this repo is not the fastest way (code need update, contribution is

Hao 448 Jan 02, 2023
An Industrial Grade Federated Learning Framework

DOC | Quick Start | 中文 FATE (Federated AI Technology Enabler) is an open-source project initiated by Webank's AI Department to provide a secure comput

Federated AI Ecosystem 4.8k Jan 09, 2023
Pytorch implementation of few-shot semantic image synthesis

Few-shot Semantic Image Synthesis Using StyleGAN Prior Our method can synthesize photorealistic images from dense or sparse semantic annotations using

40 Sep 26, 2022
Tensorflow-seq2seq-tutorials - Dynamic seq2seq in TensorFlow, step by step

seq2seq with TensorFlow Collection of unfinished tutorials. May be good for educational purposes. 1 - simple sequence-to-sequence model with dynamic u

Matvey Ezhov 1k Dec 17, 2022
Tensor-based approaches for fMRI classification

tensor-fmri Using tensor-based approaches to classify fMRI data from StarPLUS. Citation If you use any code in this repository, please cite the follow

4 Sep 07, 2022
Python scripts form performing stereo depth estimation using the HITNET model in Tensorflow Lite.

TFLite-HITNET-Stereo-depth-estimation Python scripts form performing stereo depth estimation using the HITNET model in Tensorflow Lite. Stereo depth e

Ibai Gorordo 22 Oct 20, 2022
The challenge for Quantum Coalition Hackathon 2021

Qchack 2021 Google Challenge This is a challenge for the brave 2021 qchack.io participants. Instructions Hello, intrepid qchacker, welcome to the G|o

quantumlib 18 May 04, 2022
A Fast Monotone Rotating Shallow Water model

pyRSW A Fast Monotone Rotating Shallow Water model How fast? As fast as a sustained 2 Gflop/s per core on a 2.5 GHz cpu (or 2048 Gflop/s with 1024 cor

Guillaume Roullet 13 Sep 28, 2022
SegTransVAE: Hybrid CNN - Transformer with Regularization for medical image segmentation

SegTransVAE: Hybrid CNN - Transformer with Regularization for medical image segmentation This repo is the official implementation for SegTransVAE. Seg

Nguyen Truong Hai 4 Aug 04, 2022
Easily pull telemetry data and create beautiful visualizations for analysis.

This repository is a work in progress. Anything and everything is subject to change. Porpo Table of Contents Porpo Table of Contents General Informati

Ryan Dawes 33 Nov 30, 2022
Unofficial PyTorch implementation of Neural Additive Models (NAM) by Agarwal, et al.

nam-pytorch Unofficial PyTorch implementation of Neural Additive Models (NAM) by Agarwal, et al. [abs, pdf] Installation You can access nam-pytorch vi

Rishabh Anand 11 Mar 14, 2022
LF-YOLO (Lighter and Faster YOLO) is used to detect defect of X-ray weld image.

This project is based on ultralytics/yolov3. LF-YOLO (Lighter and Faster YOLO) is used to detect defect of X-ray weld image. Download $ git clone http

26 Dec 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
Implementation of association rules mining algorithms (Apriori|FPGrowth) using python.

Association Rules Mining Using Python Implementation of association rules mining algorithms (Apriori|FPGrowth) using python. As a part of hw1 code in

Pre 2 Nov 10, 2021
The repository offers the official implementation of our paper in PyTorch.

Cloth Interactive Transformer (CIT) Cloth Interactive Transformer for Virtual Try-On Bin Ren1, Hao Tang1, Fanyang Meng2, Runwei Ding3, Ling Shao4, Phi

Bingoren 49 Dec 01, 2022
Code for the paper "Attention Approximates Sparse Distributed Memory"

Attention Approximates Sparse Distributed Memory - Codebase This is all of the code used to run analyses in the paper "Attention Approximates Sparse D

Trenton Bricken 14 Dec 05, 2022
This repo is duplication of jwyang/faster-rcnn.pytorch

Faster RCNN Pytorch This repo is duplication of jwyang/faster-rcnn.pytorch C/C++ code are removed and easier to study. Python 3.8.5 Ubuntu 20.04.1 LTS

Kim Jihwan 1 Jan 14, 2022
A naive ROS interface for visualDet3D.

YOLO3D ROS Node This repo contains a Monocular 3D detection Ros node. Base on https://github.com/Owen-Liuyuxuan/visualDet3D All parameters are exposed

Yuxuan Liu 19 Oct 08, 2022
Set of methods to ensemble boxes from different object detection models, including implementation of "Weighted boxes fusion (WBF)" method.

Set of methods to ensemble boxes from different object detection models, including implementation of "Weighted boxes fusion (WBF)" method.

1.4k Jan 05, 2023