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.
A+D Net: Training a Shadow Detector with Adversarial Shadow Attenuation, Hieu Le, Tomas F. Yago Vicente, Vu Nguyen, Minh Hoai, and Dimitris Samaras. European Conference on Computer Vision (ECCV), 2018 [
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This work was supported by the Vietnam Education Foundation, a gift from Adobe, NSF grant CNS-1718014, the Partner University Fund, and the SUNY2020 Infrastructure Transportation Security Center. The authors would also like to thank NVIDIA for GPU donation.
