# Boosting RGB-D Saliency Detection by Leveraging Unlabeled RGB Images

arXiv: https://arxiv.org/abs/2201.00100 · HTML: https://mlsanity.com/paper/2201.00100/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 31 of its 59 references that are in the map.
- First posted on arXiv 1 Jan 2022.

## Builds on (most cited first, 30 of 31)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [DeepLab: Semantic Image Segmentation with Deep Convolutional Nets, Atrous Convolution, and Fully Connected CRFs](https://mlsanity.com/paper/1606.00915/) (2016)
- [CBAM: Convolutional Block Attention Module](https://mlsanity.com/paper/1807.06521/) (2018)
- [Temporal Ensembling for Semi-Supervised Learning](https://mlsanity.com/paper/1610.02242/) (2016)
- [Structure-measure: A New Way to Evaluate Foreground Maps](https://mlsanity.com/paper/1708.00786/) (2017)
- [Enhanced-alignment Measure for Binary Foreground Map Evaluation](https://mlsanity.com/paper/1805.10421/) (2018)
- [High-Resolution Representations for Labeling Pixels and Regions](https://mlsanity.com/paper/1904.04514/) (2019)
- [Inf-Net: Automatic COVID-19 Lung Infection Segmentation from CT Images](https://mlsanity.com/paper/2004.14133/) (2020)
- [ParseNet: Looking Wider to See Better](https://mlsanity.com/paper/1506.04579/) (2015)
- [Salient Object Detection in the Deep Learning Era: An In-Depth Survey](https://mlsanity.com/paper/1904.09146/) (2019)
- [Rethinking RGB-D Salient Object Detection: Models, Data Sets, and Large-Scale Benchmarks](https://mlsanity.com/paper/1907.06781/) (2019)
- [Learning to Decompose and Disentangle Representations for Video Prediction](https://mlsanity.com/paper/1806.04166/) (2018)
- [UC-Net: Uncertainty Inspired RGB-D Saliency Detection via Conditional Variational Autoencoders](https://mlsanity.com/paper/2004.05763/) (2020)
- [JL-DCF: Joint Learning and Densely-Cooperative Fusion Framework for RGB-D Salient Object Detection](https://mlsanity.com/paper/2004.08515/) (2020)
- [Hierarchical Dynamic Filtering Network for RGB-D Salient Object Detection](https://mlsanity.com/paper/2007.06227/) (2020)
- [A Single Stream Network for Robust and Real-time RGB-D Salient Object Detection](https://mlsanity.com/paper/2007.06811/) (2020)
- [Siamese Network for RGB-D Salient Object Detection and Beyond](https://mlsanity.com/paper/2008.12134/) (2020)
- [Review of Visual Saliency Detection with Comprehensive Information](https://mlsanity.com/paper/1803.03391/) (2018)
- [Deep RGB-D Saliency Detection with Depth-Sensitive Attention and Automatic Multi-Modal Fusion](https://mlsanity.com/paper/2103.11832/) (2021)
- [Accurate RGB-D Salient Object Detection via Collaborative Learning](https://mlsanity.com/paper/2007.11782/) (2020)
- [RGB-D Salient Object Detection with Cross-Modality Modulation and Selection](https://mlsanity.com/paper/2007.07051/) (2020)
- [Cross-Modal Weighting Network for RGB-D Salient Object Detection](https://mlsanity.com/paper/2007.04901/) (2020)
- [RGBD Salient Object Detection via Deep Fusion](https://mlsanity.com/paper/1607.03333/) (2016)
- [Progressively Guided Alternate Refinement Network for RGB-D Salient Object Detection](https://mlsanity.com/paper/2008.07064/) (2020)
- [Exploring Disentangled Feature Representation Beyond Face Identification](https://mlsanity.com/paper/1804.03487/) (2018)
- [Cascade Graph Neural Networks for RGB-D Salient Object Detection](https://mlsanity.com/paper/2008.03087/) (2020)
- [Adaptive Fusion for RGB-D Salient Object Detection](https://mlsanity.com/paper/1901.01369/) (2019)
- [Co-saliency Detection for RGBD Images Based on Multi-constraint Feature Matching and Cross Label Propagation](https://mlsanity.com/paper/1710.05172/) (2017)
- [Interpreting and Disentangling Feature Components of Various Complexity from DNNs](https://mlsanity.com/paper/2006.15920/) (2020)

## Built on by (most cited first, 2 of 2)

- [Salient Object Detection From Arbitrary Modalities](https://mlsanity.com/paper/2405.03352/) (2024)
- [Dual Mutual Learning Network with Global-local Awareness for RGB-D Salient Object Detection](https://mlsanity.com/paper/2501.01648/) (2025)

## Abstract (arXiv)

Training deep models for RGB-D salient object detection (SOD) often requires a large number of labeled RGB-D images. However, RGB-D data is not easily acquired, which limits the development of RGB-D SOD techniques. To alleviate this issue, we present a Dual-Semi RGB-D Salient Object Detection Network (DS-Net) to leverage unlabeled RGB images for boosting RGB-D saliency detection. We first devise a depth decoupling convolutional neural network (DDCNN), which contains a depth estimation branch and a saliency detection branch. The depth estimation branch is trained with RGB-D images and then used to estimate the pseudo depth maps for all unlabeled RGB images to form the paired data. The saliency detection branch is used to fuse the RGB feature and depth feature to predict the RGB-D saliency. Then, the whole DDCNN is assigned as the backbone in a teacher-student framework for semi-supervised learning. Moreover, we also introduce a consistency loss on the intermediate attention and saliency maps for the unlabeled data, as well as a supervised depth and saliency loss for labeled data. Experimental results on seven widely-used benchmark datasets demonstrate that our DDCNN outperforms state-of-the-art methods both quantitatively and qualitatively. We also demonstrate that our semi-supervised DS-Net can further improve the performance, even when using an RGB image with the pseudo depth map.