  We propose a novel GAN-based framework for detecting shadows in images, in
which a shadow detection network (D-Net) is trained together with a shadow
attenuation network (A-Net) that generates adversarial training examples. The
A-Net modifies the original training images constrained by a simplified
physical shadow model and is focused on fooling the D-Net's shadow predictions.
Hence, it is effectively augmenting the training data for D-Net with
hard-to-predict cases. The D-Net is trained to predict shadows in both original
images and generated images from the A-Net. Our experimental results show that
the additional training data from A-Net significantly improves the shadow
detection accuracy of D-Net. Our method outperforms the state-of-the-art
methods on the most challenging shadow detection benchmark (SBU) and also
obtains state-of-the-art results on a cross-dataset task, testing on UCF.
Furthermore, the proposed method achieves accurate real-time shadow detection
at 45 frames per second.
