Detecting objects in low-light scenarios presents a persistent challenge, as
detectors trained on well-lit data exhibit significant performance degradation
on low-light data due to the low visibility. Previous methods mitigate this
issue by investigating image enhancement or object detection techniques using
low-light image datasets. However, the progress is impeded by the inherent
difficulties associated with collecting and annotating low-light images. To
address this challenge, we propose to boost low-light object detection with
zero-shot day-night domain adaptation, which aims to generalize a detector from
well-lit scenarios to low-light ones without requiring real low-light data. We
first design a reflectance representation learning module to learn
Retinex-based illumination invariance in images with a carefully designed
illumination invariance reinforcement strategy. Next, an
interchange-redecomposition-coherence procedure is introduced to improve over
the vanilla Retinex image decomposition process by performing two sequential
image decompositions and introducing a redecomposition cohering loss. Extensive
experiments on ExDark, DARK FACE and CODaN datasets show strong low-light
generalizability of our method.

通过零样本白天 - 夜晚领域自适应方法，我们在不需要真实低光照数据的情况下，提出了一种增强低光物体检测的方法。我们设计了一个反射率表示学习模块，通过仔细设计的光照不变性增强策略，学习了基于 Retinex 的图像不变性。同时，我们引入了一个互换重组相干步骤，通过进行两次顺序图像重组和引入重组相干损失来改进传统 Retinex 图像分解过程。在 ExDark、DARK FACE 和 CODaN 数据集上进行了大量实验，验证了我们方法的强大低光照普适性。