Papers › Learning Synergistic Attention for Light Field Salient Object Detection
Learning Synergistic Attention for Light Field Salient Object Detection
Yi Zhang, Geng Chen, Qian Chen, Yujia Sun, Yong Xia, Olivier Deforges, Wassim Hamidouche, Lu Zhang
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.
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