  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.
