We propose a novel Synergistic Attention Network (SA-Net) to address the light field salient object detection by establishing a synergistic effect between multi-modal features with advanced attention mechanisms. Our SA-Net exploits the rich information of focal stacks via 3D convolutional neural networks, decodes the high-level features of multi-modal light field data with two cascaded synergistic attention modules, and predicts the saliency map using an effective feature fusion module in a progressive manner. Extensive experiments on three widely-used benchmark datasets show that our SA-Net outperforms 28 state-of-the-art models, sufficiently demonstrating its effectiveness and superiority. Our code is available at https://github.com/PanoAsh/SA-Net.
@article{arxiv.2104.13916,
title = {Learning Synergistic Attention for Light Field Salient Object Detection},
author = {Yi Zhang and Geng Chen and Qian Chen and Yujia Sun and Yong Xia and Olivier Deforges and Wassim Hamidouche and Lu Zhang},
journal= {arXiv preprint arXiv:2104.13916},
year = {2021}
}