@inproceedings{fa1e224ee32a495482bf75ac7ca642f1,
title = "Salient Object Detection with Capsule-Based Conditional Generative Adversarial Network",
abstract = "Salient Object Detection (SOD) is one significant research area which is closely correlated to the attention of human beings. Most of the nowadays CNN-based approaches for SOD are based on an U-Net architecture. In this paper, we propose a novel capsule-based salient object detection framework by integrating the novel capsule blocks into both the generator and discriminator of GAN architecture. The experimental result showed that our approach is able to generate accurate saliency maps, which also highlighted the effectiveness of the capsule blocks. We also provide a challenging dataset that contains 3,299 images for SOD with difficult foreground objects and complex background contents.",
keywords = "Capsule Net, Generative Adversarial Network, Image-level Saliency, Salient Object Detection, cGAN",
author = "Chao Zhang and Fei Yang and Guoping Qiu and Qian Zhang",
note = "Publisher Copyright: {\textcopyright} 2019 IEEE.; 26th IEEE International Conference on Image Processing, ICIP 2019 ; Conference date: 22-09-2019 Through 25-09-2019",
year = "2019",
month = sep,
doi = "10.1109/ICIP.2019.8802915",
language = "English",
series = "Proceedings - International Conference on Image Processing, ICIP",
publisher = "IEEE Computer Society",
pages = "81--85",
booktitle = "2019 IEEE International Conference on Image Processing, ICIP 2019 - Proceedings",
address = "United States",
}