$^3$ Net: Self-Supervised Self-Ensembling Network for Semi-Supervised RGB-D Salient Object Detection | IEEE Transactions on Multimedia"/>

research-article

S <inline-formula><tex-math notation="LaTeX">$^3$</tex-math></inline-formula> Net: Self-Supervised Self-Ensembling Network for Semi-Supervised RGB-D Salient Object Detection

Authors:

Xiaoqiang Wang,

Carola-Bibiane Schönlieb,

C. L. Philip ChenAuthors Info & Claims

IEEE Transactions on Multimedia, Volume 25

Pages 676 - 689

https://doi.org/10.1109/TMM.2021.3129730

Published: 01 January 2023 Publication History

Abstract

RGB-D salient object detection aims to detect visually distinctive objects or regions from a pair of the RGB image and the depth image. State-of-the-art RGB-D saliency detectors are mainly based on convolutional neural networks but almost suffer from an intrinsic limitation relying on the labeled data, thus degrading detection accuracy in complex cases. In this work, we present a self-supervised self-ensembling network (S <inline-formula><tex-math notation="LaTeX">$^3$</tex-math></inline-formula> Net) for semi-supervised RGB-D salient object detection by leveraging the unlabeled data and exploring a self-supervised learning mechanism. To be specific, we first build a self-guided convolutional neural network (SG-CNN) as a baseline model by developing a series of three-layer cross-model feature fusion (TCF) modules to leverage complementary information among depth and RGB modalities and formulating an auxiliary task that predicts a self-supervised image rotation angle. After that, to further explore the knowledge from unlabeled data, we assign SG-CNN to a student network and a teacher network, and encourage the saliency predictions and self-supervised rotation predictions from these two networks to be consistent on the unlabeled data. Experimental results on seven widely-used benchmark datasets demonstrate that our network quantitatively and qualitatively outperforms the state-of-the-art methods.

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IEEE Transactions on Multimedia Volume 25, Issue

2023

8932 pages

ISSN:1520-9210

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Cited By

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Wang JLi GQiu GMa GXi JYu N(2024)Depth-Assisted Semi-Supervised RGB-D Rail Surface Defect InspectionIEEE Transactions on Intelligent Transportation Systems10.1109/TITS.2024.338794925:7(8042-8052)Online publication date: 1-Jul-2024
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Meena PKumar HYadav S(2024)A Volumetric Saliency Guided Image Summarization for RGB-D Indoor Scene ClassificationIEEE Transactions on Circuits and Systems for Video Technology10.1109/TCSVT.2024.341294934:11_Part_1(10917-10929)Online publication date: 11-Jun-2024
https://dl.acm.org/doi/10.1109/TCSVT.2024.3412949
Han XYou MMa WJin CHe LSong MWang R(2023)Semi-supervised Learning with Easy Labeled Data via Impartial Labeled Set ExtensionProceedings of the 1st International Workshop on Multimedia Content Generation and Evaluation: New Methods and Practice10.1145/3607541.3616815(29-39)Online publication date: 29-Oct-2023
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Lin JWang L(2022)Spatial-temporal Fusion Network for Fast Video Shadow DetectionProceedings of the 18th ACM SIGGRAPH International Conference on Virtual-Reality Continuum and its Applications in Industry10.1145/3574131.3574455(1-5)Online publication date: 27-Dec-2022
https://dl.acm.org/doi/10.1145/3574131.3574455

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