Saliency-Guided Image Translation
Lai Jiang, Mai Xu, Xiaofei Wang, Leonid Sigal
Abstract
In this paper, we propose a novel task for saliency-guided image translation, with the goal of image-to-image translation conditioned on the user specified saliency map. To address this problem, we develop a novel Generative Adversarial Network (GAN)-based model, called SalG-GAN. Given the original image and target saliency map, SalG-GAN can generate a translated image that satisfies the target saliency map. In SalG-GAN, a disentangled representation framework is proposed to encourage the model to learn diverse translations for the same target saliency condition. A saliency-based attention module is introduced as a special attention mechanism for facilitating the developed structures of saliency-guided generator, saliency cue encoder and saliency-guided global and local discriminators. Furthermore, we build a synthetic dataset and a real-world dataset with labeled visual attention for training and evaluating our SalG-GAN. The experimental results over both datasets verify the effectiveness of our model for saliency-guided image translation.