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Salient object detection (SOD) in RGB and depth images has attracted increasing research interest. Existing RGB-D SOD models usually adopt fusion strategies to learn a shared representation from RGB and depth modalities, while few methods explicitly consider how to preserve modality-specific characteristics. In this study, we propose a novel framework, the specificity-preserving network (SPNet), which improves SOD performance by exploring both the shared information and modality-specific properties. Specifically, we use two modality-specific networks and a shared learning network to generate individual and shared saliency prediction maps. To effectively fuse cross-modal features in the shared learning network, we propose a cross-enhanced integration module (CIM) and propagate the fused feature to the next layer to integrate cross-level information. Moreover, to capture rich complementary multi-modal information to boost SOD performance, we use a multi-modal feature aggregation (MFA) module to integrate the modality-specific features from each individual decoder into the shared decoder. By using skip connections between encoder and decoder layers, hierarchical features can be fully combined. Extensive experiments demonstrate that our SPNet outperforms cutting-edge approaches on six popular RGB-D SOD and three camouflaged object detection benchmarks. The project is publicly available at https://github.com/taozh2017/SPNet.


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Specificity-preserving RGB-D saliency detection

Show Author's information Tao Zhou1,2Deng-Ping Fan3( )Geng Chen4Yi Zhou5Huazhu Fu6
School of Computer Science and Engineering, Nanjing University of Science and Technology, Nanjing 210094, China
Key Laboratory of System Control and Information Processing, Ministry of Education, Shanghai, China
Computer Vision Lab, ETH Zürich, Zürich, Switzerland
School of Computer Science and Engineering, North-western Polytechnical University, Xi’an, China
School of Computer Science and Engineering, SoutheastUniversity, Nanjing, China
Inception Institute of Artificial Intelligence, Abu Dhabi, United Arab Emirates

Abstract

Salient object detection (SOD) in RGB and depth images has attracted increasing research interest. Existing RGB-D SOD models usually adopt fusion strategies to learn a shared representation from RGB and depth modalities, while few methods explicitly consider how to preserve modality-specific characteristics. In this study, we propose a novel framework, the specificity-preserving network (SPNet), which improves SOD performance by exploring both the shared information and modality-specific properties. Specifically, we use two modality-specific networks and a shared learning network to generate individual and shared saliency prediction maps. To effectively fuse cross-modal features in the shared learning network, we propose a cross-enhanced integration module (CIM) and propagate the fused feature to the next layer to integrate cross-level information. Moreover, to capture rich complementary multi-modal information to boost SOD performance, we use a multi-modal feature aggregation (MFA) module to integrate the modality-specific features from each individual decoder into the shared decoder. By using skip connections between encoder and decoder layers, hierarchical features can be fully combined. Extensive experiments demonstrate that our SPNet outperforms cutting-edge approaches on six popular RGB-D SOD and three camouflaged object detection benchmarks. The project is publicly available at https://github.com/taozh2017/SPNet.

Keywords:

salient object detection (SOD), RGB-D, cross-enhanced integration module (CIM), multi-modal feature aggregation (MFA)
Received: 19 October 2021 Accepted: 01 January 2022 Published: 03 January 2023 Issue date: June 2023
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Publication history

Received: 19 October 2021
Accepted: 01 January 2022
Published: 03 January 2023
Issue date: June 2023

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© The Author(s) 2022.

Acknowledgements

This work was supported in part by the National Natural Science Foundation of China under Grant No. 62172228, in part by an Open Project of the Key Laboratory of System Control and Information Processing, Ministry of Education (Shanghai Jiao Tong University, No. Scip202102).

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