  Low light conditions not only degrade human visual experience, but also
reduce the performance of downstream machine analytics. Although many works
have been designed for low-light enhancement or domain adaptive machine
analytics, the former considers less on high-level vision, while the latter
neglects the potential of image-level signal adjustment. How to restore
underexposed images/videos from the perspective of machine vision has long been
overlooked. In this paper, we are the first to propose a learnable illumination
enhancement model for high-level vision. Inspired by real camera response
functions, we assume that the illumination enhancement function should be a
concave curve, and propose to satisfy this concavity through discrete integral.
With the intention of adapting illumination from the perspective of machine
vision without task-specific annotated data, we design an asymmetric
cross-domain self-supervised training strategy. Our model architecture and
training designs mutually benefit each other, forming a powerful unsupervised
normal-to-low light adaptation framework. Comprehensive experiments demonstrate
that our method surpasses existing low-light enhancement and adaptation methods
and shows superior generalization on various low-light vision tasks, including
classification, detection, action recognition, and optical flow estimation.
Project website: https://daooshee.github.io/SACC-Website/
