Learn-to-Decompose: Cascaded Decomposition Network for Cross-Domian Few-Shot Facial Expression Recignition
This is an official implementation of the following paper:
Learn-to-Decompose: Cascaded Decomposition Network for Cross-Domian Few-Shot Facial Expression Recignition
Xinyi Zou, Yan Yan*, Jing-Hao Xue, Si Chen, and Hanzi Wang
European Conference on Computer Vision (ECCV), 2022
In breif, we have the following contributions:
- propose CDNet which cascades several shared learn-to-decompose (LD) module via a sequential decomposition mechanism to obtain the general expression prototypes and their corresponing weights.
- develop a partial regularization strategy to combine the benefits of both episodic training and batch training
- achieve stat-of-the-art performance on various compound FER datasets under CD-FSL setting.
Please cite our paper if you find the code useful for your research.
@article{zou2022learn,
title={Learn-to-Decompose: Cascaded Decomposition Network for Cross-Domain Few-Shot Facial Expression Recognition},
author={Zou, Xinyi and Yan, Yan and Xue, Jing-Hao and Chen, Si and Wang, Hanzi},
journal={arXiv preprint arXiv:2207.07973},
year={2022}
}
- Python >= 3.7
- Pytorch >= 1.7 and torchvision (https://pytorch.org/)
- You can use the
requirements.txtfile we provide to setup the environment via Anaconda.
conda create --name py37 python=3.7
conda install pytorch torchvision -c pytorch
pip3 install -r requirements.txt
Clone this repository:
git clone https://github.com/zouxinyi0625/CDNet.git
cd CDNet
-
Basic Expression Datasets
-
Compound Expression Datasets
-
Download the above FER datasets, and generate the json file with the instruction here.
- Our model is trained on the multiple basic expression datasets, and tested on various compound FER datasets (only the compound subsets).
- All the images in the basic expression datasets are used for training, and all the images in the compound expression datastes are used for testing.
We adopt the pretrained ResNet18 from here.
- Pretrain (Batch Training)
cd batch
python pretrain_e.py --dataset multi --name cascade_e --testset CFEE --split val --color 3 --w_domain 1.0
- Finetune (Episodic Training)
cd episodic
python train_l2d_pre.py --dataset multi --testset CFEE --split val --train_aug --weight net --name cascade_pre --w_d 1.0 --w_t 1.0 --pretrain
- This code is built upon the implementation from CloserLookFewShot, CrossDomainFewShot, AssociateAlignment. We greatly thank for the authors for their contributions.
- The dataset, model, and code are for non-commercial research purposes only.
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