Learning Synergistic Attention for Light Field Salient Object Detection

• URL: http://arxiv.org/abs/2104.13916v2
• Date: Fri, 30 Apr 2021 14:39:06 GMT
• Title: Learning Synergistic Attention for Light Field Salient Object Detection
• Authors: Yi Zhang, Geng Chen, Qian Chen, Yujia Sun, Olivier Deforges, Wassim Hamidouche and Lu Zhang
• Abstract summary: We propose a novel Synergistic Attention Network (SA-Net) to address the light field salient object detection. 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, and predicts the saliency map.
• Score: 20.53608130346838
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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 will be made publicly available.

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