Paired low/normal-light images with instance-level pixel-wise annotations. Real-world low-light instance segmentation dataset comprising over two thousand InĪddition, to facilitate further research in this direction, we capture a Low-light RAW synthetic pipeline to generate realistic low-light data. To mitigate the scarcity of annotated RAW datasets, we leverage a OurĪnalysis indicates that high bit-depth can be critical for low-light instance Preserve richer scene information in low-light conditions compared to typicalĬamera sRGB outputs, thus supporting the use of RAW-input algorithms. Furthermore, we discover that high-bit-depth RAW images can better These components effectively reduce feature noise during downsampling andĬonvolution operations, enabling the model to learn disturbance-invariantįeatures. Smooth-oriented convolutional block, and disturbance suppression learning. Learning method that relies on an adaptive weighted downsampling layer, a At times sublime, strange and deeply human Adrian Tchaikovsky, bestselling and award-winning author of the Children of Time series As ambitious as it is intimate, How High We Go in the Dark is both a prescient warning and a promise of human resilience in the face of any odds. To suppress this ``feature noise', we propose a novel Observation that noise in low-light images introduces high-frequencyĭisturbances to the feature maps of neural networks, thereby significantlyĭegrading performance. Segmentation in the dark and introduce several techniques that substantiallyīoost the low-light inference accuracy.

In this work, we take a deep look at instance In this work, we take a deep look at instance segmentation in the dark. Existing instance segmentation techniques are primarily tailored for high-visibility inputs, but their performance significantly deteriorates in extremely low-light environments.

High-visibility inputs, but their performance significantly deteriorates inĮxtremely low-light environments. Linwei Chen, Ying Fu, Kaixuan Wei, Dezhi Zheng, Felix Heide. Download a PDF of the paper titled Instance Segmentation in the Dark, by Linwei Chen and 4 other authors Download PDF Abstract: Existing instance segmentation techniques are primarily tailored for

How high we go on the dark