Learning Synergistic Attention for Light Field Salient Object Detection

by   Yi Zhang, et al.
NetEase, Inc

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.


page 1

page 3

page 6

page 8

page 9

page 10

page 11

page 12


CMA-Net: A Cascaded Mutual Attention Network for Light Field Salient Object Detection

In the past few years, numerous deep learning methods have been proposed...

Context-aware Cross-level Fusion Network for Camouflaged Object Detection

Camouflaged object detection (COD) is a challenging task due to the low ...

Guided Focal Stack Refinement Network for Light Field Salient Object Detection

Light field salient object detection (SOD) is an emerging research direc...

OGNet: Salient Object Detection with Output-guided Attention Module

Attention mechanisms are widely used in salient object detection models ...

Light Field Salient Object Detection: A Review and Benchmark

Salient object detection (SOD) is a long-standing research topic in comp...

4D-Net for Learned Multi-Modal Alignment

We present 4D-Net, a 3D object detection approach, which utilizes 3D Poi...

GroupTransNet: Group Transformer Network for RGB-D Salient Object Detection

Salient object detection on RGB-D images is an active topic in computer ...

Code Repositories

Please sign up or login with your details

Forgot password? Click here to reset