  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/
