HLA-Face: Joint High-Low Adaptation for Low Light Face Detection

Wenjing Wang, Wenhan Yang, Jiaying Liu; Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2021, pp. 16195-16204

Abstract


Face detection in low light scenarios is challenging but vital to many practical applications, e.g., surveillance video, autonomous driving at night. Most existing face detectors heavily rely on extensive annotations, while collecting data is time-consuming and laborious. To reduce the burden of building new datasets for low light conditions, we make full use of existing normal light data and explore how to adapt face detectors from normal light to low light. The challenge of this task is that the gap between normal and low light is too huge and complex for both pixel-level and object-level. Therefore, most existing low-light enhancement and adaptation methods do not achieve desirable performance. To address the issue, we propose a joint High-Low Adaptation (HLA) framework. Through a bidirectional low-level adaptation and multi-task high-level adaptation scheme, our HLA-Face outperforms state-of-the-art methods even without using dark face labels for training. Our project is publicly available at: https://daooshee.github.io/HLA-Face-Website/

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[bibtex]
@InProceedings{Wang_2021_CVPR, author = {Wang, Wenjing and Yang, Wenhan and Liu, Jiaying}, title = {HLA-Face: Joint High-Low Adaptation for Low Light Face Detection}, booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)}, month = {June}, year = {2021}, pages = {16195-16204} }