![]() 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 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 High-visibility inputs, but their performance significantly deteriorates inĮxtremely low-light environments. 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 ![]()
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