  Existing instance segmentation techniques are primarily tailored for
high-visibility inputs, but their performance significantly deteriorates in
extremely low-light environments. In this work, we take a deep look at instance
segmentation in the dark and introduce several techniques that substantially
boost the low-light inference accuracy. The proposed method is motivated by the
observation that noise in low-light images introduces high-frequency
disturbances to the feature maps of neural networks, thereby significantly
degrading performance. To suppress this ``feature noise", we propose a novel
learning method that relies on an adaptive weighted downsampling layer, a
smooth-oriented convolutional block, and disturbance suppression learning.
These components effectively reduce feature noise during downsampling and
convolution operations, enabling the model to learn disturbance-invariant
features. Furthermore, we discover that high-bit-depth RAW images can better
preserve richer scene information in low-light conditions compared to typical
camera sRGB outputs, thus supporting the use of RAW-input algorithms. Our
analysis indicates that high bit-depth can be critical for low-light instance
segmentation. To mitigate the scarcity of annotated RAW datasets, we leverage a
low-light RAW synthetic pipeline to generate realistic low-light data. In
addition, to facilitate further research in this direction, we capture a
real-world low-light instance segmentation dataset comprising over two thousand
paired low/normal-light images with instance-level pixel-wise annotations.
Remarkably, without any image preprocessing, we achieve satisfactory
performance on instance segmentation in very low light (4~\% AP higher than
state-of-the-art competitors), meanwhile opening new opportunities for future
research.
