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Progressively Guided Alternate Refinement Network for RGB-D Salient Object Detection

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 12353)

Abstract

In this paper, we aim to develop an efficient and compact deep network for RGB-D salient object detection, where the depth image provides complementary information to boost performance in complex scenarios. Starting from a coarse initial prediction by a multi-scale residual block, we propose a progressively guided alternate refinement network to refine it. Instead of using ImageNet pre-trained backbone network, we first construct a lightweight depth stream by learning from scratch, which can extract complementary features more efficiently with less redundancy. Then, different from the existing fusion based methods, RGB and depth features are fed into proposed guided residual (GR) blocks alternately to reduce their mutual degradation. By assigning progressive guidance in the stacked GR blocks within each side-output, the false detection and missing parts can be well remedied. Extensive experiments on seven benchmark datasets demonstrate that our model outperforms existing state-of-the-art approaches by a large margin, and also shows superiority in efficiency (71 FPS) and model size (64.9 MB).

Keywords

RGB-D salient object detection Lightweight depth stream Alternate refinement Progressive guidance 

Notes

Acknowledgments

This research was supported by the National Nature Science Foundation of China (No. 61802336) and China Scholarship Council (CSC) Program. This work was mainly done when Shuhan Chen was visiting Northeastern University as a visiting scholar.

Supplementary material

504445_1_En_31_MOESM1_ESM.pdf (752 kb)
Supplementary material 1 (pdf 752 KB)

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Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.School of Information EngineeringYangzhou UniversityYangzhouChina
  2. 2.Department of ECE and Khoury College of Computer ScienceNortheastern UniversityBostonUSA

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