Our paper is accepted by ICCV2021.

Picture: Overview of the proposed Plug-and-Play (PnP) adaption framework for generalizing gaze estimation to a new domain.

Picture: The proposed architecture.

Results

This repository contains the official PyTorch implementation of the following paper:

Generalizing Gaze Estimation with Outlier-guided Collaborative Adaptation
Yunfei Liu, Ruicong Liu, Haofei Wang, Feng Lu

Abstract: Deep neural networks have significantly improved appearance-based gaze estimation accuracy. However, it still suffers from unsatisfactory performance when generalizing the trained model to new domains, e.g., unseen environments or persons. In this paper, we propose a plugand-play gaze adaptation framework (PnP-GA), which is an ensemble of networks that learn collaboratively with the guidance of outliers. Since our proposed framework does not require ground-truth labels in the target domain, the existing gaze estimation networks can be directly plugged into PnP-GA and generalize the algorithms to new domains. We test PnP-GA on four gaze domain adaptation tasks, ETH-to-MPII, ETH-to-EyeDiap, Gaze360-to-MPII, and Gaze360-to-EyeDiap. The experimental results demonstrate that the PnP-GA framework achieves considerable performance improvements of 36.9%, 31.6%, 19.4%, and 11.8% over the baseline system. The proposed framework also outperforms the state-of-the-art domain adaptation approaches on gaze domain adaptation tasks.

Material related to our paper is available via the following links:

• Paper: https://arxiv.org/abs/2107.13780
• Project: https://liuyunfei.net/publication/iccv2021_pnp-ga/
• Code: https://github.com/DreamtaleCore/PnP-GA

• Only Linux is tested, Windows is under test.
• 64-bit Python 3.6 installation.

You need to modify the config.yaml first, especially xxx/image, xxx/label, and xxx_pretrains params.

xxx/image represents the path of label file.

xxx/root represents the path of image file.

xxx_pretrains represents the path of pretrained models.

A example of label file is data folder. Each line in label file is conducted as:

p00/face/1.jpg 0.2558059438789034,-0.05467275933864655 -0.05843388117618364,0.46745964684693614 ... ...

Where our code reads image data form os.path.join(xxx/root, "p00/face/1.jpg") and reads ground-truth labels of gaze direction from the rest in label file.

We provide three optional arguments, which are --oma2, --js and --sg. They repersent three different network components, which could be found in our paper.

--source and --target represent the datasets used as the source domain and the target domain. You can choose among eth, gaze360, mpii, edp.

--i represents the index of person which is used as the training set. You can set it as -1 for using all the person as the training set.

--pics represents the number of target domain samples for adaptation.

We also provide other arguments for adjusting the hyperparameters in our PnP-GA architecture, which could be found in our paper.

For example, you can run the code like:

python3 adapt.py --i 0 --pics 10 --savepath path/to/save --source eth --target mpii --gpu 0 --js --oma2 --sg

--i, --savepath, --target are the same as training.

--p represents the index of person which is used as the training set in the adaptation process.

For example, you can run the code like:

python3 test.py --i -1 --p 0 --savepath path/to/save --target mpii

If you find this work or code is helpful in your research, please cite:

@inproceedings{liu2021PnP_GA,
  title={Generalizing Gaze Estimation with Outlier-guided Collaborative Adaptation},
  author={Liu, Yunfei and Liu, Ruicong and Wang, Haofei and Lu, Feng},
  booktitle={Proceedings of the IEEE/CVF International Conference on Computer Vision},
  year={2021}
}

If you have any questions, feel free to E-mail me via: lyunfei(at)buaa.edu.cn