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Found 3 papers, 2 papers with code
FIFO: Learning Fog-invariant Features for Foggy Scene Segmentation
Robust visual recognition under adverse weather conditions is of great importance in real-world applications.
Ranked #4 on
Domain Adaptation
on Cityscapes-to-FoggyDriving
WEDGE: Web-Image Assisted Domain Generalization for Semantic Segmentation
We also present a method which injects styles of the web-crawled images into training images on-the-fly during training, which enables the network to experience images of diverse styles with reliable labels for effective training.
URIE: Universal Image Enhancement for Visual Recognition in the Wild
Despite the great advances in visual recognition, it has been witnessed that recognition models trained on clean images of common datasets are not robust against distorted images in the real world.
