@inproceedings{47963455021241e5828c43e0c1838ae5,
title = "A+D Net: Training a Shadow Detector with Adversarial Shadow Attenuation",
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{\textquoteright}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.",
keywords = "Data augmentation, GAN, Shadow detection",
author = "Hieu Le and Vicente, \{Tomas F.Yago\} and Vu Nguyen and Minh Hoai and Dimitris Samaras",
note = "Publisher Copyright: {\textcopyright} 2018, Springer Nature Switzerland AG.; 15th European Conference on Computer Vision, ECCV 2018 ; Conference date: 08-09-2018 Through 14-09-2018",
year = "2018",
doi = "10.1007/978-3-030-01216-8\_41",
language = "English",
isbn = "9783030012151",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Verlag",
pages = "680--696",
editor = "Martial Hebert and Yair Weiss and Vittorio Ferrari and Cristian Sminchisescu",
booktitle = "Computer Vision – ECCV 2018 - 15th European Conference, 2018, Proceedings",
}