  Domain generalization for semantic segmentation is highly demanded in real
applications, where a trained model is expected to work well in previously
unseen domains. One challenge lies in the lack of data which could cover the
diverse distributions of the possible unseen domains for training. In this
paper, we propose a WEb-image assisted Domain GEneralization (WEDGE) scheme,
which is the first to exploit the diversity of web-crawled images for
generalizable semantic segmentation. To explore and exploit the real-world data
distributions, we collect web-crawled images which present large diversity in
terms of weather conditions, sites, lighting, camera styles, etc. 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. Moreover,
we use the web-crawled images with their predicted pseudo labels for training
to further enhance the capability of the network. Extensive experiments
demonstrate that our method clearly outperforms existing domain generalization
techniques.
