Salient object detection with adversarial training

Abstract

The generative adversarial network has been shown to produce state-of-the-art results of image generation. In this study, the authors propose a novel adversarial training method to train salient object detection (SOD) models. They train a convolutional SOD network along with a gated adversarial network that discriminates salient maps coming either from the ground truth or from the SOD network. The motivation for our approach is that the adversarial network can detect and correct pixel-wise errors between ground truth salient detection maps and the ones produced by the convolutional network. Our experiments show that the adversarial training approach leads to state-of-the-art performance on MSRA-B, extended complex scene saliency dataset, HKU-IS, DUT, and SOD dataset.