Abstract. 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 stateof-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