Instance Segmentation in the Dark

Noname manuscript No. (will be inserted by the editor) Abstract Existing instance segmentation techniques are primarily depth can be critical for low-light instance segmentation. To tailored for high-visibility inputs, but their performance mitigate the scarcity of annotated RAW datasets, we leverage significantly deteriorates in extremely low-light environments. In addition, to facilitate further research in the dark and introduce several techniques that in this direction, we capture a real-world low-light instance substantially boost the low-light inference accuracy. The proposed segmentation dataset comprising over two thousand paired method is motivated by the observation that noise in low/normal-light images with instance-level pixel-wise annotations. To suppress this "feature noise", we in very low light (4 % AP higher than state-of-the-art propose a novel learning method that relies on an adaptive competitors), meanwhile opening new opportunities for future weighted downsampling layer, a smooth-oriented convolutional research. Our code and dataset are publicly available to block, and disturbance suppression learning. Furthermore, we discover that high-bit-depth RAW images can better preserve richer scene information in low-light conditions compared 1 Introduction to typical camera sRGB outputs, thus supporting the use of RAW-input algorithms. "buried" by severe noise caused by limited photon count and They substantially improve the capability of models to learn noiseresisted features and thus boost the low-light segmentation accuracy appreciably. It is worth noting that they are modelagnostic and lightweight or even cost-free. It aggregates local features adaptively and suppresses the high-frequency disturbance caused by noise as well as keeping the details in deep features. The smoothoriented convolutional block enhances the ordinary convolutional layers by adding a smooth-oriented convolution branch. Relevant Moreover, we notice that the high bit-depth can be crucial low-light recognition/detection methods (Cui et al., 2021; for low-light conditions.