  Most prior semantic segmentation methods have been developed for day-time
scenes, while typically underperforming in night-time scenes due to
insufficient and complicated lighting conditions. In this work, we tackle this
challenge by proposing a novel night-time semantic segmentation paradigm, i.e.,
disentangle then parse (DTP). DTP explicitly disentangles night-time images
into light-invariant reflectance and light-specific illumination components and
then recognizes semantics based on their adaptive fusion. Concretely, the
proposed DTP comprises two key components: 1) Instead of processing
lighting-entangled features as in prior works, our Semantic-Oriented
Disentanglement (SOD) framework enables the extraction of reflectance component
without being impeded by lighting, allowing the network to consistently
recognize the semantics under cover of varying and complicated lighting
conditions. 2) Based on the observation that the illumination component can
serve as a cue for some semantically confused regions, we further introduce an
Illumination-Aware Parser (IAParser) to explicitly learn the correlation
between semantics and lighting, and aggregate the illumination features to
yield more precise predictions. Extensive experiments on the night-time
segmentation task with various settings demonstrate that DTP significantly
outperforms state-of-the-art methods. Furthermore, with negligible additional
parameters, DTP can be directly used to benefit existing day-time methods for
night-time segmentation.
