Age-Aware Guidance via Masking-Based Attention in Face Aging

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

Age-Aware Guidance via Masking-Based Attention in Face Aging
Junyeong Maeng^1,*, Kwanseok Oh^1,*, and Heung-Il Suk^{1, 2}
(¹Department of Artificial Intelligence, Korea University)
(²Department of Brain and Cognitive Engineering, Korea University)
(* indicates equal contribution)
Official Version: https://doi.org/10.1145/3583780.3615183
Published in 32nd ACM International Conference on Information and Knowledge Management (CIKM), At: Birmingham, UK

Abstract: Face age transformation aims to convert reference images into synthesized images so that they portray the specified target ages. The crux of this task is to change only age-related areas of the given image while maintaining the age-irrelevant areas unchanged. Nevertheless, a common limitation among most existing models is the struggle to generate high-quality aging images that effectively consider both crucial properties. To address this problem, we propose a novel GAN-based face-aging framework that utilizes age-aware Guidance via Masking-Based Attention (GMBA). Specifically, we devise an age-aware guidance module to adjust age-relevant and age-irrelevant attributes within the image seamlessly. By virtue of its capability, it enables the model to produce realistic age-transformed images that certainly preserve the input's identities while delicately imposing age-related properties. Experimental results show that our proposed GMBA outperformed other state-of-the-art methods in terms of identity preservation and accurate age conversion, as well as providing superior visual quality for age-transformed images.

Setup

• Python 3.7.10
• CUDA Version 11.0

1. Nvidia driver, CUDA toolkit 11.0, install Anaconda.

2. Install pytorch

conda install pytorch torchvision cudatoolkit=11.0 -c pytorch

4. Install various necessary packages

pip install numpy tqdm

Training

When using Terminal, directly execute the code below after setting the path

python main.py --gpu 0 --batch_size 64 --epoch 100

Citation

If used in your research, please cite the following paper:

@inproceedings{maeng2023age,
  title={Age-Aware Guidance via Masking-Based Attention in Face Aging},
  author={Maeng, Junyeong and Oh, Kwanseok and Suk, Heung-Il},
  booktitle={Proceedings of the 32nd ACM International Conference on Information and Knowledge Management},
  pages={4165--4169},
  year={2023}
}

Acknowledgement

This work was supported by Institute of Information & communications Technology Planning & Evaluation (IITP) grant funded by the Korea government (MSIT) No. 2019-0-00079 (Artificial Intelligence Graduate School Program(Korea University)) and No. 2022- 0-00959 ((Part 2) Few-Shot Learning of Causal Inference in Vision and Language for Decision Making).