FeatEnHancer: Enhancing Hierarchical Features for Object Detection and
  Beyond Under Low-Light Vision
Khurram Azeem Hashmi Goutham Kallempudi Didier Stricker Muhammamd Zeshan Afzal
Extracting useful visual cues for the downstream tasks is especially
challenging under low-light vision. Prior works create enhanced representations
by either correlating visual quality with machine perception or designing
illumination-degrading transformation methods that require pre-training on
synthetic datasets. We argue that optimizing enhanced image representation
pertaining to the loss of the downstream task can result in more expressive
representations. Therefore, in this work, we propose a novel module,
FeatEnHancer, that hierarchically combines multiscale features using
multiheaded attention guided by task-related loss function to create suitable
representations. Furthermore, our intra-scale enhancement improves the quality
of features extracted at each scale or level, as well as combines features from
different scales in a way that reflects their relative importance for the task
at hand. FeatEnHancer is a general-purpose plug-and-play module and can be
incorporated into any low-light vision pipeline. We show with extensive
experimentation that the enhanced representation produced with FeatEnHancer
significantly and consistently improves results in several low-light vision
tasks, including dark object detection (+5.7 mAP on ExDark), face detection
(+1.5 mAPon DARK FACE), nighttime semantic segmentation (+5.1 mIoU on ACDC ),
and video object detection (+1.8 mAP on DarkVision), highlighting the
effectiveness of enhancing hierarchical features under low-light vision.