CVPR 2021

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

Wenjing Wang     Wenhan Yang     Jiaying Liu



Motivation: comparison of different adaptive low light detection techniques. L: low light data. H: normal light data. Existing enhancement-based, darkening-based, and feature adaptation methods either ignore the high-level gap, or have limited effects due to the huge and complex gap between L and H. Our method instead considers both low-level and high-level adaptation, therefore achieves better performance.

Framework: LOW-LEVEL adaptation fills the gap by creating intermediate states. We bidirectionally brighten the low light data as well as distort the normal light data with noise and color bias. Based on the built intermediate states, we use multi-task cross-domain self-supervised learning to fill the HIGH-LEVEL gap.

Selected Experimental Results

Precision-Recall (PR) curves on DARK FACE.

Qualitative comparison of different enhancement-based methods. (a) Input low light image and the ground truth boxes. (b)-(g) Results of low-light enhancement methods with DSFD [1]. (h) Our result.



    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}


[1] Jian Li, Yabiao Wang, Changan Wang, Ying Tai, Jianjun Qian, Jian Yang, Chengjie Wang, Jilin Li, Feiyue Huang: DSFD: Dual Shot Face Detector. CVPR 2019: 5060-5069

[2] Xiaojie Guo, Yu Li, Haibin Ling: LIME: Low-Light Image Enhancement via Illumination Map Estimation. IEEE Trans. Image Process. 26(2): 982-993 (2017)