  Instance segmentation for low-light imagery remains largely unexplored due to
the challenges imposed by such conditions, for example shot noise due to low
photon count, color distortions and reduced contrast. In this paper, we propose
an end-to-end solution to address this challenging task. Based on Mask R-CNN,
our proposed method implements weighted non-local (NL) blocks in the feature
extractor. This integration enables an inherent denoising process at the
feature level. As a result, our method eliminates the need for aligned ground
truth images during training, thus supporting training on real-world low-light
datasets. We introduce additional learnable weights at each layer in order to
enhance the network's adaptability to real-world noise characteristics, which
affect different feature scales in different ways.
  Experimental results show that the proposed method outperforms the pretrained
Mask R-CNN with an Average Precision (AP) improvement of +10.0, with the
introduction of weighted NL Blocks further enhancing AP by +1.0.
