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https://github.com/taozh2017/RGBD-SODsurvey

RGB-D Salient Object Detection: A Survey
https://github.com/taozh2017/RGBD-SODsurvey
benchmark light-field light-field-sod rgb-d saliency salient-object-detection sod survey
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RGB-D Salient Object Detection: A Survey
Host: GitHub
URL: https://github.com/taozh2017/RGBD-SODsurvey
Owner: taozh2017
Created: 2020-06-15T08:28:21.000Z (about 4 years ago)
Default Branch: master
Last Pushed: 2023-08-01T14:45:50.000Z (11 months ago)
Last Synced: 2024-02-21T10:31:58.582Z (4 months ago)
Topics: benchmark, light-field, light-field-sod, rgb-d, saliency, salient-object-detection, sod, survey
Language: MATLAB
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Forks: 28
Open Issues: 5
Metadata Files:
- Readme: README.md
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awesome-salient-object-detection - RGB-D SOD Survey
awesomeai - RGB-D Salient Object Detection
README

        # [RGB-D Salient Object Detection: A Survey](https://arxiv.org/abs/2008.00230) 

Authors: [*Tao Zhou*](https://taozh2017.github.io), [*Deng-Ping Fan*](https://dpfan.net/), [*Ming-Ming Cheng*](https://mmcheng.net/), [*Jianbing Shen*](https://scholar.google.com/citations?user=_Q3NTToAAAAJ&hl=en), [*Ling Shao*](https://scholar.google.com/citations?user=z84rLjoAAAAJ&hl=en).

This is a survey to review related RGB-D SOD models along with benchmark datasets, and provide a comprehensive evaluation for these models. We also collect related review papers for SOD and light field SOD models. If you have papers to recommend or any suggestions, please feel free to contact us.

:running: :running: :running: ***KEEP UPDATING***.   

  

  

![alt text](./figures/Fig0.jpg)

*Fig.0: A brief chronology of RGB-D based SOD. The first early RGB-D based SOD work was the [DM](https://link.springer.com/content/pdf/10.1007/978-3-642-33709-3_8.pdf) model, proposed in 2012. Deep learning

techniques have been widely applied to RGB-D based SOD since 2017. More details can be found in our [paper](https://arxiv.org/pdf/2008.00230.pdf).*

------

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## Content:

1.  Related Reviews and Surveys to SOD 

2.  RGB-D SOD Models 

3.  RGB-D SOD Datasets 

4.   Light Field SOD 

    1.   LF SOD Models 

    1.   LF Datasets 

5.  Evaluation 

    1.   Overall Evaluation 

    1.   Attribute-based Evaluation 

6.  RGB-D SOD Benchmark        

7.  Citation 

------

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## Related Reviews and Surveys to SOD:    

**No.** | **Year** | **Pub.** | **Title** | **Links** 

:-: | :-: | :-: | :-  | :-: 

01 | 2015 | IEEE TIP   |  Salient object detection: A benchmark | [Paper](https://arxiv.org/pdf/1501.02741.pdf)/Project

02 | 2018 | IEEE TCSVT |  Review of visual saliency detection with comprehensive information | [Paper](https://arxiv.org/pdf/1803.03391.pdf)/Project

03 | 2018 | ACM TIST   |  A review of co-saliency detection algorithms: Fundamentals, applications, and challenges | [Paper](https://arxiv.org/pdf/1604.07090.pdf)/Project

04 | 2018 | IEEE TSP   |  Advanced deep-learning techniques for salient and category-specific object detection: A survey| [Paper](https://ieeexplore.ieee.org/document/8253582)/Project

05 | 2018 | IJCV       |  Attentive systems: A survey | [Paper](https://link.springer.com/article/10.1007/s11263-017-1042-6)/Project

06 | 2018 | ECCV       |  Salient Objects in Clutter: Bringing Salient Object Detection to the Foreground | [Paper](http://mftp.mmcheng.net/Papers/18ECCV-SOCBenchmark.pdf)/[Project](https://github.com/DengPingFan/SODBenchmark/)

07 | 2019 | CVM        |  Salient object detection: A survey | [Paper](https://link.springer.com/content/pdf/10.1007/s41095-019-0149-9.pdf)/Project

08 | 2019 | IEEE TNNLS |  Object detection with deep learning: A review | [Paper](https://arxiv.org/pdf/1807.05511.pdf)/Project

09 | 2020 | arXiv      |  Light Field Salient Object Detection: A Review and Benchmark | [Paper](https://arxiv.org/pdf/2010.04968.pdf)/[Project](https://github.com/kerenfu/LFSOD-Survey) 

10 | 2021 | IEEE TPAMI      |  Salient Object Detection in the Deep Learning Era: An In-Depth Survey | [Paper](https://arxiv.org/pdf/1904.09146.pdf)/[Project](https://github.com/wenguanwang/SODsurvey) 

------

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## RGB-D SOD Models:    

:fire::fire::fire:Update (in 2023-07-26)

**No.** | **Year** | **Model** |**Pub.** | **Title** | **Links** 

:-: | :-: | :-: | :-  | :-  | :-: 

:fire: 219 | 2023 |XMSNet| ACM MM | Object Segmentation by Mining Cross-Modal Semantics | [Paper](https://arxiv.org/pdf/2305.10469.pdf)/[Project](https://github.com/Zongwei97/XMSNet)

:fire: 218 | 2023 |PopNet| ICCV | Source-free Depth for Object Pop-out | [Paper](https://arxiv.org/pdf/2212.05370.pdf)/[Project](https://github.com/Zongwei97/PopNet)

:fire: 217 | 2023 |FCFNet| IEEE TSCVT | Feature Calibrating and Fusing Network for RGB-D Salient Object Detection | [Paper](https://ieeexplore.ieee.org/abstract/document/10185946)/Project

:fire: 216 | 2023 |AirSOD| IEEE TSCVT | AirSOD: A Lightweight Network for RGB-D Salient Object Detection | [Paper](https://ieeexplore.ieee.org/abstract/document/10184101)/Project

:fire: 215 | 2023 |CATNet| IEEE TMM | CATNet: A Cascaded and Aggregated Transformer Network For RGB-D Salient Object Detection | [Paper](https://ieeexplore.ieee.org/abstract/document/10179145)/Project

214 | 2023 |--| arXiv | Hierarchical Cross-modal Transformer for RGB-D Salient Object Detection  | [Paper](https://arxiv.org/pdf/2302.08052.pdf)/Project 

213| 2023 |HiDAnet| IEEE TIP | HiDAnet: RGB-D Salient Object Detection via Hierarchical Depth Awareness  | [Paper](https://ieeexplore.ieee.org/document/10091765)/[Project](https://github.com/Zongwei97/HIDANet/)

212 | 2023 |AFNet| Neurocomputing | Adaptive fusion network for RGB-D salient object detection  | [Paper](https://www.sciencedirect.com/science/article/pii/S0925231222015090)/[Project](https://github.com/clelouch/AFNet)

211 | 2023 |EGA-Net| Information Sciences | EGA-Net: Edge feature enhancement and global information attention network for RGB-D salient object detection  | [Paper](https://www.sciencedirect.com/science/article/pii/S0020025523000324)/[Project](https://github.com/guanyuzong/EGA-Net)

210 | 2022 |RFNet| 3DV | Robust RGB-D Fusion for Saliency Detection | [Paper](https://ieeexplore.ieee.org/document/10044460)/[Project](https://github.com/Zongwei97/RFnet)

209 | 2022 |--|IEEE TIP| Improving RGB-D Salient Object Detection via Modality-Aware Decoder   | [Paper](https://ieeexplore.ieee.org/abstract/document/9894275)/[Project](https://github.com/MengkeSong/MaD)

208 | 2022 |--| ICIP | Multi-Modal Transformer for RGB-D Salient Object Detection  | [Paper](https://ieeexplore.ieee.org/abstract/document/9898069)/Project

207 | 2022 |--| AI | A cascaded refined rgb-d salient object detection network based on the attention mechanism  | [Paper](https://link.springer.com/article/10.1007/s10489-022-04186-9)/Project

206 | 2022 |EFGNet| DSP | EFGNet: Encoder steered multi-modality feature guidance network for RGB-D salient object detection  | [Paper](https://www.sciencedirect.com/science/article/pii/S105120042200392X)/Project

205 | 2022 |--| ICIP | Dynamic Selection Network For Rgb-D Salient Object Detection  | [Paper](https://ieeexplore.ieee.org/abstract/document/9897821)/Project

204 | 2022 |--| IEEE SPL | Cross-stage Multi-scale Interaction Network for RGB-D Salient Object Detection | [Paper](https://ieeexplore.ieee.org/abstract/document/9956739)/Project

203 | 2022 |GCENet| JVCIR | GCENet: Global contextual exploration network for RGB-D salient object detection | [Paper](https://www.sciencedirect.com/science/article/pii/S1047320322002000)/Project

202 | 2022 |SiamRIR|ACCV | Multi-scale Residual Interaction for RGB-D Salient Object Detection  | [Paper](https://openaccess.thecvf.com/content/ACCV2022/papers/Hu_Multi-scale_Residual_Interaction_for_RGB-D_Salient_Object_Detection_ACCV_2022_paper.pdf)/Project

201 | 2022 |SAFNet| ACCV | Scale Adaptive Fusion Network for RGB-D Salient Object Detection  | [Paper](https://openaccess.thecvf.com/content/ACCV2022/papers/Kong_Scale_Adaptive_Fusion_Network_for_RGB-D_Salient_Object_Detection_ACCV_2022_paper.pdf)/Project

200 | 2022 |TBINet|ACCV| Three-Stage Bidirectional Interaction Network for Efficient RGB-D Salient Object Detection  | [Paper](https://openaccess.thecvf.com/content/ACCV2022/papers/Wang_Three-Stage_Bidirectional_Interaction_Network_for_Efficient_RGB-D_Salient_Object_Detection_ACCV_2022_paper.pdf)/[Project](https://github.com/AWORKERINKIKIMORE/TBINet)

199 | 2022 |--|Neurocomputing| Few-shot learning-based RGB-D salient object detection: A case study  | [Paper](https://www.sciencedirect.com/science/article/pii/S0925231222011055)/Project

198 | 2022 |--|ACM MM | Depth-inspired Label Mining for Unsupervised RGB-D Salient Object Detection  | [Paper](https://dl.acm.org/doi/pdf/10.1145/3503161.3548037)/Project

197 | 2022 |RD3D|IEEE TNNLS | 3-D Convolutional Neural Networks for RGB-D Salient Object Detection and Beyond  | [Paper](https://ieeexplore.ieee.org/abstract/document/9889257)/[Project](https://github.com/QianChen98/RD3D)

196 | 2022 |--| IEEE TIP | Middle-Level Feature Fusion for Lightweight RGB-D Salient Object Detection  | [Paper](https://ieeexplore.ieee.org/abstract/document/9923611)/Project

195 | 2022 |--| Neurocomputing | Depth-aware inverted refinement network for RGB-D salient object detection  | [Paper](https://www.sciencedirect.com/science/article/pii/S0925231222014126)/Project

194 | 2022 |DCMNet| ESA | DCMNet: Discriminant and cross-modality network for RGB-D salient object detection   | [Paper](https://www.sciencedirect.com/science/article/pii/S0957417422020656)/Project

193 | 2022 |HINet| PR | Cross-modal hierarchical interaction network for RGB-D salient object detection   | [Paper](https://www.sciencedirect.com/science/article/pii/S0031320322006732)/[Project](https://github.com/RanwanWu/HINet)

192 | 2022 |CIR-Net| IEEE TIP | CIR-Net: Cross-modality interaction and refinement for RGB-D salient object detection   | [Paper](https://ieeexplore.ieee.org/abstract/document/9930882)/[arXiv](https://arxiv.org/pdf/2210.02843.pdf)/[Project](https://rmcong.github.io/proj_CIRNet.html)

191 | 2022 |MVSalNet | ECCV | MVSalNet:Multi-View Augmentation for RGB-D Salient Object Detection  | [Paper](https://www.ecva.net/papers/eccv_2022/papers_ECCV/papers/136890268.pdf)/[Project](https://github.com/Heart-eartH/MVSalNet)

190 | 2022 |SPSN     | ECCV | SPSN: Superpixel Prototype Sampling Network for RGB-D Salient Object Detection  | [Paper](https://arxiv.org/pdf/2207.07898.pdf)/[Project](https://github.com/Hydragon516/SPSN)

189 | 2022 |RLLNet     | SCIS | RLLNet: a lightweight remaking learning network for saliency redetection on RGB-D images  | [Paper](https://link.springer.com/article/10.1007/s11432-020-3337-9)/Project

188 | 2022 |-| IEEE TIP | Learning Implicit Class Knowledge for RGB-D Co-Salient Object Detection with Transformers  | [Paper](https://ieeexplore.ieee.org/abstract/document/9810116)/Project

187 | 2022 |-| MTAP | A benchmark dataset and baseline model for co-salient object detection within RGB-D images | [Paper](https://link.springer.com/article/10.1007/s11042-021-11555-y)/Project

186 | 2022 |SA-DPNet| PR | SA-DPNet: Structure-aware dual pyramid network for salient object detection | [Paper](https://www.sciencedirect.com/science/article/pii/S0031320322001054)/Project

185 | 2022 |-| JPCS | RGBD salient object detection based on depth feature enhancement  | [Paper](https://iopscience.iop.org/article/10.1088/1742-6596/2181/1/012008/meta)/Project

184 | 2022 |-| JCSC | Bifurcation Fusion Network for RGB-D Salient Object Detection  | [Paper](https://www.worldscientific.com/doi/abs/10.1142/S0218126622502152)/Project

183 | 2022 |-| arXiv | Dynamic Message Propagation Network for RGB-D Salient Object Detection   | [Paper](https://arxiv.org/pdf/2206.09552.pdf)/Project

182 | 2022 |-| IEEE TMM | Depth-induced Gap-reducing Network for RGB-D Salient Object Detection: An Interaction, Guidance and Refinement Approach   | [Paper](https://ieeexplore.ieee.org/abstract/document/9769984)/Project

181 | 2022 |-| arXiv | Dual Swin-Transformer based Mutual Interactive Network for RGB-D Salient Object Detection   | [Paper](https://arxiv.org/pdf/2206.03105.pdf)/Project

180 | 2022 |MoADNet| IEEE TCSVT | MoADNet: Mobile Asymmetric Dual-Stream Networks for Real-Time and Lightweight RGB-D Salient Object Detection  | [Paper](https://ieeexplore.ieee.org/abstract/document/9789193)/Project

179 | 2022 |A2TPNet| Electronics | A2TPNet: Alternate Steered Attention and Trapezoidal Pyramid Fusion Network for RGB-D Salient Object Detection  | [Paper](https://www.mdpi.com/2079-9292/11/13/1968)/Project

178 | 2022 |--| NPL | Depth Enhanced Cross-Modal Cascaded Network for RGB-D Salient Object Detection  | [Paper](https://link.springer.com/article/10.1007/s11063-022-10886-7)/Project

177 | 2022 |--| KBS | Boosting RGB-D salient object detection with adaptively cooperative dynamic fusion network  | [Paper](https://www.sciencedirect.com/science/article/pii/S0950705122005998)/Project

176 | 2022 |C2DFNet| IEEE TMM | C2DFNet: Criss-Cross Dynamic Filter Network for RGB-D Salient Object Detection  | [Paper](https://ieeexplore.ieee.org/abstract/document/9813422)/[Project](https://github.com/OIPLab-DUT/C2DFNet)

175 | 2022 |MEANet| Neurocomputing | MEANet: Multi-modal edge-aware network for light field salient object detection  | [Paper](https://www.sciencedirect.com/science/article/abs/pii/S0925231222003502)/[Project](https://github.com/jiangyao-scu/MEANet)

174 | 2022 |--| arXiv | Depth-Cooperated Trimodal Network for Video Salient Object Detection   | [Paper](https://arxiv.org/abs/2202.06060)/Project

173 | 2022 |DFTR| arXiv | DFTR: Depth-supervised Fusion Transformer for Salient Object Detection  | [Paper](https://arxiv.org/abs/2203.06429)/Project

172 | 2022 |--| JP: CS | Multi-level interactions for RGB-D object detection | [Paper](https://iopscience.iop.org/article/10.1088/1742-6596/2181/1/012003/meta)/Project

171 | 2022 |--| AAAI |  Self-Supervised Pretraining for RGB-D Salient Object Detection | [Paper](https://www.aaai.org/AAAI22Papers/AAAI-4882.ZhaoX.pdf)/[Project](https://github.com/Xiaoqi-Zhao-DLUT/SSLSOD)

170 | 2022 |GroupTransNet| arXiv | GroupTransNet: Group Transformer Network for RGB-D Salient Object Detection | [Paper](https://arxiv.org/abs/2203.10785)/Project

169 | 2022 |GroupTransNet| arXiv | GroupTransNet: Group Transformer Network for RGB-D Salient Object Detection | [Paper](https://arxiv.org/abs/2203.10785)/Project

168 | 2022 |BGRDNet| MTAP | BGRDNet: RGB-D salient object detection with a bidirectional gated recurrent decoding network  | [Paper](https://link.springer.com/article/10.1007/s11042-022-12799-y)/Project

167 | 2022 |LIANet| IEEE Access| LIANet: Layer Interactive Attention Network for RGB-D Salient Object Detectionn  | [Paper](https://ieeexplore.ieee.org/abstract/document/9729233)/Project

166 | 2022 |AGRFNet| SP:IC| AGRFNet: Two-stage cross-modal and multi-level attention gated recurrent fusion network for RGB-D saliency detection  | [Paper](https://www.sciencedirect.com/science/article/abs/pii/S0923596522000261)/Project

165 | 2022 |--| IEEE TIP| Weakly Supervised RGB-D Salient Object Detection With Prediction Consistency Training and Active Scribble Boosting  | [Paper](https://ieeexplore.ieee.org/abstract/document/9720104)/Project

164 | 2022 |--| PR| Discriminative unimodal feature selection and fusion for RGB-D salient object detection  | [Paper](https://www.sciencedirect.com/science/article/pii/S0031320321005392)/Project

163 | 2022 |MIA_DPD| Neurocomputing| Multi-modal interactive attention and dual progressive decoding network for RGB-D/T salient object detection  | [Paper](https://www.sciencedirect.com/science/article/pii/S0925231222002971)/[Project](https://github.com/Liangyh18/MIA_DPD)

162 | 2022 |--| PR| Discriminative unimodal feature selection and fusion for RGB-D salient object detection  | [Paper](https://www.sciencedirect.com/science/article/pii/S0031320321005392)/Project

161 | 2022 |--| ESA| Aggregate interactive learning for RGB-D salient object detection  | [Paper](https://www.sciencedirect.com/science/article/pii/S0957417422001051)/Project

160 | 2022 |FCMNet| Neurocomputing| FCMNet: Frequency-aware cross-modality attention networks for RGB-D salient object detection  | [Paper](https://www.sciencedirect.com/science/article/pii/S0925231222003848)/Project

159 | 2022 |--| PR| Encoder Deep Interleaved Network with Multi-scale Aggregation for RGB-D Salient Object Detection  | [Paper](https://www.sciencedirect.com/science/article/pii/S0031320322001479)/Project

158 | 2022 |--| ICLR| Promoting Saliency From Depth: Deep Unsupervised RGB-D Saliency Detection  | [Paper](https://openreview.net/pdf?id=BZnnMbt0pW)/Project

157 | 2022 |FANet| SP:IC| FANet: Feature aggregation network for RGBD saliency detection  | [Paper](https://www.sciencedirect.com/science/article/pii/S0923596521002964)/Project

156 | 2022 |DCMF | IEEE TIP| Learning Discriminative Cross-Modality Features for RGB-D Saliency Detection  | [Paper](https://ieeexplore.ieee.org/abstract/document/9678058)/[Project](https://github.com/fereenwong/DCMF)

155 | 2022 |DS-Net | IEEE TIP| Boosting RGB-D Saliency Detection by Leveraging Unlabeled RGB Images | [Paper](https://ieeexplore.ieee.org/abstract/document/9673131)/Project

154 | 2022 |CFIDNet | NCA| CFIDNet: cascaded feature interaction decoder for RGB-D salient object detection | [Paper](https://link.springer.com/article/10.1007/s00521-021-06845-3)/[Project](https://github.com/clelouch/CFIDNet)

153 | 2022 |-- | IVC| Double cross-modality progressively guided network for RGB-D salient object detection | [Paper](https://www.sciencedirect.com/science/article/pii/S0262885621002560)/Project

152 | 2022 |-- | PR|Discriminative unimodal feature selection and fusion for RGB-D salient object detection  | [Paper](https://www.sciencedirect.com/science/article/pii/S0031320321005392)/Project

151 | 2022 |BPGNet | IEEE TCSVT |Bi-directional Progressive Guidance Network for RGB-D Salient Object Detection    | [Paper](https://ieeexplore.ieee.org/abstract/document/9686679)/Project

150 | 2022 |-- | ESA |Aggregate interactive learning for RGB-D salient object detection   | [Paper](https://www.sciencedirect.com/science/article/pii/S0957417422001051)/Project

149 | 2021 |MobileSal | IEEE TPAMI | MobileSal: Extremely Efficient RGB-D Salient Object Detection     | [Paper](https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9647954)/[Project](https://github.com/yuhuan-wu/mobilesal)

148 | 2021 |PGFNet | IEEE TNNLS |RGB-D Point Cloud Registration Based on Salient Object Detection   | [Paper](https://ieeexplore.ieee.org/abstract/document/9350205)/Project

147 | 2021 |M2RNet | arXiv |M2RNet: Multi-modal and Multi-scale Refined Network for RGB-D Salient Object Detection  | [Paper](https://arxiv.org/abs/2109.07922)/Project

146 | 2021 |MGSNet | 3DV |Modality-Guided Subnetwork for Salient Object Detection | [Paper](https://ieeexplore.ieee.org/document/9665910)/[Project](https://github.com/Zongwei97/MGSnet)

145 | 2021 |-- | TVC |Guided residual network for RGB-D salient object detection with efficient depth feature learning | [Paper](https://link.springer.com/article/10.1007/s00371-021-02106-5)/Project

144 | 2021 |-- | Neurocomputing |A cross-modal edge-guided salient object detection for RGB-D image | [Paper](https://www.sciencedirect.com/science/article/pii/S0925231221007244)/Project

143 | 2021 |-- | CEI |A deep multimodal feature learning network for RGB-D salient object detection | [Paper](https://www.sciencedirect.com/science/article/pii/S004579062100029X)/Project

142 | 2021 |PGFNet | IEEE Access |Progressive Guided Fusion Network With Multi-Modal and Multi-Scale Attention for RGB-D Salient Object Detection  | [Paper](https://ieeexplore.ieee.org/abstract/document/9606676)/Project

141 | 2021 |-- | TVC |Multi-level progressive parallel attention guided salient object detection for RGB-D images  | [Paper](https://link.springer.com/article/10.1007/s00371-020-01821-9)/Project

140 | 2021 |-- | EAAI | Multi-scale iterative refinement network for RGB-D salient object detection     | [Paper](https://www.sciencedirect.com/science/article/pii/S0952197621003213)/Project

139 | 2021 |-- | IEEE TMM | Employing Bilinear Fusion and Saliency Prior Information for RGB-D Salient Object Detection     | [Paper](https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9392336)/Project

138 | 2021 |-- | PR | Context-aware network for RGB-D salient object detection    | [Paper](https://www.sciencedirect.com/science/article/pii/S0031320320304337)/Project

137 | 2021 |DSNet | IEEE TIP | Dynamic Selective Network for RGB-D Salient Object Detection    | [Paper](https://ieeexplore.ieee.org/abstract/document/9605221)/Project

136 | 2021 |ACFNet | arXiv | ACFNet: Adaptively-Cooperative Fusion Network for RGB-D Salient Object Detection   | [Paper](https://arxiv.org/abs/2109.04627)/Project

135 | 2021 |AFI-Net | CIN | AFI-Net: Attention-Guided Feature Integration Network for RGBD Saliency Detection  | [Paper](https://www.hindawi.com/journals/cin/2021/8861446/)/Project

134 | 2021 |JSM | NIPS | Joint Semantic Mining for Weakly Supervised RGB-D Salient Object Detection  | [Paper](https://papers.nips.cc/paper/2021/file/642e92efb79421734881b53e1e1b18b6-Paper.pdf)/[Project](https://github.com/jiwei0921/JSM)

133 | 2021 |PGFNet | IEEE Access | Progressive Guided Fusion Network With Multi-Modal and Multi-Scale Attention for RGB-D Salient Object Detection  | [Paper](https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9606676)/Project

132 | 2021 |-- | PR | Discriminative unimodal feature selection and fusion for RGB-D salient object detection  | [Paper](https://www.sciencedirect.com/science/article/pii/S0031320321005392)/Project

131 | 2021 |UTA | IEEE TIP| RGB-D Salient Object Detection With Ubiquitous Target Awareness  | [Paper](https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9529069)/Project

130 | 2021 |SP-Net  | ICCV| Specificity-preserving RGB-D Saliency Detection  | [Paper](https://arxiv.org/pdf/2108.08162.pdf)/[Project](https://github.com/taozh2017/SPNet)

129 | 2021 |--      | ICCV| RGB-D Saliency Detection via Cascaded Mutual Information Minimization  | Paper/[Project](https://github.com/JingZhang617/cascaded_rgbd_sod)

128 | 2021 |TMFNet   | IEEE TETCI| TMFNet: Three-Input Multilevel Fusion Network for Detecting Salient Objects in RGB-D Images  | [Paper](https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9512550)/Project

127 | 2021 |--   | SP| Multiscale multilevel context and multimodal fusion for RGB-D salient object detection  | [Paper](https://www.sciencedirect.com/science/article/pii/S0165168420303091)/Project

126 | 2021 |--   | IEEE TMM |Employing Bilinear Fusion and Saliency Prior Information for RGB-D Salient Object Detection  | [Paper](https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9392336)/Project

125 | 2021 |CAN  | PR        |Context-aware network for RGB-D salient object detection  | [Paper](https://www.sciencedirect.com/science/article/pii/S0031320320304337)/Project

124 | 2021 |--   | Neurocomputing |Rethinking feature aggregation for deep RGB-D salient object detection  | [Paper](https://www.sciencedirect.com/science/article/pii/S0925231220316921)/Project

123 | 2021 |--   | Neurocomputing |Circular Complement Network for RGB-D Salient Object Detection  | [Paper](https://www.sciencedirect.com/science/article/pii/S0925231221005944)/Project

122 | 2021 | LSF   | IJCV          | CNN-based RGB-D Salient Object Detection: Learn, Select and Fuse | [Paper](https://arxiv.org/pdf/1909.09309.pdf)/Project

121 | 2021 |TriTransNet  | ACM MM | TriTransNet RGB-D Salient Object Detection with a Triplet Transformer Embedding Network  | Paper/[Project](https://github.com/liuzywen/TriTransNet)

120 | 2021 |CDINet  | ACM MM | Cross-modality Discrepant Interaction Network for RGB-D Salient Object Detection  | Paper/Project

119 | 2021 |DFM-Net  | ACM MM | Depth Quality-Inspired Feature Manipulation for Efficient RGB-D Salient Object Detection  | [Paper](https://arxiv.org/pdf/2107.01779.pdf)/[Project](https://github.com/zwbx/DFM-Net)

118 | 2021 |VST  | arXiv | Visual Saliency Transformer   | [Paper](https://arxiv.org/pdf/2104.12099.pdf)/Project

117 | 2021 |--  | arXiv | Progressive Multi-scale Fusion Network for RGB-D Salient Object Detection  | [Paper](https://arxiv.org/pdf/2106.03941.pdf)/Project

116 | 2021 |--  | arXiv | Dynamic Knowledge Distillation with A Single Stream Structure for RGB-D Salient Object Detection  | [Paper](https://arxiv.org/pdf/2106.09517.pdf)/Project

115 | 2021 |MRINet  | SPL | MRINet: Multilevel Reverse-Context Interactive-Fusion Network for Detecting Salient Objects in RGB-D Images  | [Paper](https://ieeexplore.ieee.org/abstract/document/9466383)/Project

114 | 2021 |DSA^2F  | CVPR | Deep RGB-D Saliency Detection with Depth-Sensitive Attention and Automatic Multi-Modal Fusion  | [Paper](https://openaccess.thecvf.com/content/CVPR2021/papers/Sun_Deep_RGB-D_Saliency_Detection_With_Depth-Sensitive_Attention_and_Automatic_Multi-Modal_CVPR_2021_paper.pdf)/[Project](https://github.com/sunpeng1996/DSA2F)

113 | 2021 |DCF  | CVPR | Calibrated RGB-D Salient Object Detection  | [Paper](https://openaccess.thecvf.com/content/CVPR2021/papers/Ji_Calibrated_RGB-D_Salient_Object_Detection_CVPR_2021_paper.pdf)/[Project](https://github.com/jiwei0921/DCF)

112 | 2021 |BTS-Net  | ICME| BTS-Net: Bi-directional Transfer-and-Selection Network for RGB-D Salient Object Detection  | Paper/[Project](https://github.com/zwbx/BTS-Net)

111 | 2021 |CDNet  | IEEE TIP| CDNet: Complementary Depth Network for RGB-D Salient Object Detection  | [Paper](https://ieeexplore.ieee.org/abstract/document/9366409)/[Project](https://github.com/blanclist/CDNet)

110 | 2021 |CCAFNet  | IEEE TMM | CCAFNet: Crossflow and Cross-scale Adaptive Fusion Network for Detecting Salient Objects in RGB-D Images  | [Paper](https://ieeexplore.ieee.org/abstract/document/9424966)/Project

109 | 2021 |ShuffeNet  | arXiv | Middle-level Fusion for Lightweight RGB-D Salient Object Detection  | [Paper](https://arxiv.org/pdf/2104.11543.pdf)/Project

108 | 2021 |--  | The Visual Computer | Guided residual network for RGB-D salient object detection with efficient depth feature learning  | [Paper](https://link.springer.com/article/10.1007/s00371-021-02106-5)/Project

107 | 2021 |--  | IEEE TIP | Hierarchical Alternate Interaction Network for RGB-D Salient Object Detection  | [Paper](https://ieeexplore.ieee.org/abstract/document/9371407)/[Project](https://github.com/MathLee/HAINet)

106 | 2021 |AFLNet  | SP: IC  | AFLNet: Adversarial focal loss network for RGB-D salient object detection  | [Paper](https://www.sciencedirect.com/science/article/pii/S0923596521000497)/Project

105 | 2021 |--   | arXiv   | Self-Supervised Representation Learning for RGB-D Salient Object Detection | [Paper](https://arxiv.org/pdf/2101.12482.pdf)/Project

104 | 2021 |--   | Sensors | Saliency Detection with Bilateral Absorbing Markov Chain Guided by Depth Information | [Paper](https://www.mdpi.com/1424-8220/21/3/838)/Project

103 | 2021 |BPA-Net    | DSP   | Boundary-aware pyramid attention network for detecting salient objects in RGB-D images | [Paper](https://www.sciencedirect.com/science/article/pii/S1051200421000142)/Project

102 | 2021 |MobileSal    | arXiv   | MobileSal: Extremely Efficient RGB-D Salient Object Detection | [Paper](https://arxiv.org/abs/2012.13095)/Project

101 | 2021 |--    | The Visual Computer    | A robust RGBD saliency method with improved probabilistic contrast and the global reference surface | [Paper](https://link.springer.com/article/10.1007/s00371-020-02050-w)/Project

100 | 2021 |RD3D    | AAAI   | RGB-D Salient Object Detection via 3D Convolutional Neural | Paper/[Project](https://github.com/PPOLYpubki/RD3D)

99 | 2021 |WGI-Net    | CVM   | WGI-Net: A weighted group integration network for RGB-D salient object detection | [Paper](https://link.springer.com/article/10.1007/s41095-020-0200-x)/Project

98 | 2021 |JL-DCF   | IEEE TPAMI  | Siamese Network for RGB-D Salient Object Detection and Beyond | [Paper](https://arxiv.org/pdf/2008.12134.pdf)/[Project](https://github.com/kerenfu/JLDCF)

97 | 2020 |CRACE    | arXiv   | A Unified Structure for Efficient RGB and RGB-D Salient Object Detection | [Paper](https://arxiv.org/pdf/2012.00437.pdf)/Project

96 | 2020 |EF-Net   | PR      | EF-Net: A novel enhancement and fusion network for RGB-D saliency detection | [Paper](https://www.sciencedirect.com/science/article/abs/pii/S0031320320305434)/Project

95 | 2020 |SMAC   | arXiv (CVPR extension)   | Learning Selective Mutual Attention and Contrast for RGB-D Saliency Detection| [Paper](https://arxiv.org/pdf/2010.05537.pdf)/[Project](https://github.com/nnizhang/SMAC)

94 | 2020 |DCMF   | IEEE TIP   | RGBD Salient Object Detection via Disentangled Cross-Modal Fusion| [Paper](https://ieeexplore.ieee.org/document/9165931)/[Project](https://github.com/haochen593/Disen_Fuse_TIP2020)

93 | 2020 |MMNet  | ACM MM  | MMNet: Multi-Stage and Multi-Scale Fusion Network for RGB-D Salient Object Detection | [Paper](https://dl.acm.org/doi/pdf/10.1145/3394171.3413523)/Project

92 | 2020 |DASNet  | ACM MM        | Is depth really necessary for salient object detection? | [Paper](https://arxiv.org/pdf/2006.00269.pdf)/[Project](http://cvteam.net/projects/2020/DASNet/)

91 | 2020 |FRDT    | ACM MM        | Feature Reintegration over Differential Treatment: A Top-down and Adaptive Fusion Network for RGB-D Salient Object Detection | [Paper](https://dl.acm.org/doi/pdf/10.1145/3394171.3413969)/[Project](https://github.com/jack-admiral/ACM-MM-FRDT)

90 | 2020 | HANet   | Appl. Sci.       | Hybrid‐Attention Network for RGB‐D Salient Object Detection | Paper/Project

89 | 2020 | DQSD   | IEEE TIP        | Depth Quality Aware Salient Object Detection | [Paper](https://arxiv.org/pdf/2008.04159.pdf)/[Project](https://github.com/qdu1995/DQSD)

88 | 2020 | DQAM   | arXiv         | Knowing Depth Quality In Advance: A Depth Quality Assessment Method For RGB-D Salient Object Detection | [Paper](https://arxiv.org/pdf/2008.04157.pdf)/Project

87 | 2020 | DRLF   | IEEE TIP      | Data-Level Recombination and Lightweight Fusion Scheme for RGB-D Salient Object Detection | [Paper](http://probb268dca.pic5.ysjianzhan.cn/upload/TIP20_WXH_q02i.pdf)/[Project](https://github.com/XueHaoWang-Beijing/DRLF)

86 | 2020 | MCINet | arXiv         | MCINet: Multi-level Cross-modal Interaction Network for RGB-D Salient Object Detection | [Paper](https://arxiv.org/pdf/2007.14352.pdf)/Project

85 | 2020 | PGAR   | ECCV          | Progressively Guided Alternate Refinement Network for RGB-D Salient Object Detection | [Paper](https://arxiv.org/pdf/2008.07064.pdf)/[Project](https://github.com/ShuhanChen/PGAR_ECCV20)

84 | 2020 | ATSA   | ECCV          | Asymmetric Two-Stream Architecture for Accurate RGB-D Saliency Detection	| [Paper](https://www.ecva.net/papers/eccv_2020/papers_ECCV/papers/123730375.pdf)/[Project](https://github.com/sxfduter/ATSA)

83 | 2020 | BBS-Net| ECCV          | BBS-Net: RGB-D Salient Object Detection with a Bifurcated Backbone Strategy Network | [Paper](https://arxiv.org/pdf/2007.02713.pdf)/[Project](https://github.com/zyjwuyan/BBS-Net)

82 | 2020 | CoNet  | ECCV          | Accurate RGB-D Salient Object Detection via Collaborative Learning | [Paper](https://arxiv.org/pdf/2007.11782.pdf)/[Project](https://github.com/jiwei0921/CoNet)

81 | 2020 | DANet  | ECCV          | A Single Stream Network for Robust and Real-time RGB-D Salient Object Detection | [Paper](https://arxiv.org/pdf/2007.06811.pdf)/[Project](https://github.com/Xiaoqi-Zhao-DLUT/DANet-RGBD-Saliency)

80 | 2020 | CMMS   | ECCV          | RGB-D salient object detection with cross-modality modulation and selection | [Paper](https://arxiv.org/pdf/2007.07051.pdf)/[Project](https://github.com/Li-Chongyi/cmMS-ECCV20)

79 | 2020 | CAS-GNN| ECCV          | Cascade graph neural networks for RGB-D salient object detection | [Paper](https://arxiv.org/pdf/2008.03087.pdf)/[Project](https://github.com/LA30/Cas-Gnn)

78 | 2020 | HDFNet | ECCV          | Hierarchical Dynamic Filtering Network for RGB-D Salient Object Detection | [Paper](https://arxiv.org/pdf/2007.06227.pdf)/[Project](https://github.com/lartpang/HDFNet)

77 | 2020 | CMWNet | ECCV          | Cross-modal weighting network for RGB-D salient object detection | [Paper](https://arxiv.org/pdf/2007.04901.pdf)/[Project](https://github.com/MathLee/CMWNet)

76 | 2020 | UC-Net | CVPR          | UC-Net: Uncertainty Inspired RGB-D Saliency Detection via Conditional Variational Autoencoders | [Paper](https://openaccess.thecvf.com/content_CVPR_2020/papers/Zhang_UC-Net_Uncertainty_Inspired_RGB-D_Saliency_Detection_via_Conditional_Variational_Autoencoders_CVPR_2020_paper.pdf)/[Project](https://github.com/JingZhang617/UCNet)

75 | 2020 | S2MA   | CVPR          | Learning selective self-mutual attention for RGB-D saliency detection | [Paper](https://openaccess.thecvf.com/content_CVPR_2020/papers/Liu_Learning_Selective_Self-Mutual_Attention_for_RGB-D_Saliency_Detection_CVPR_2020_paper.pdf)/[Project](https://github.com/nnizhang/S2MA)

74 | 2020 | SSF    | CVPR          | Select, supplement and focus for RGB-D saliency detection | [Paper](https://openaccess.thecvf.com/content_CVPR_2020/papers/Zhang_Select_Supplement_and_Focus_for_RGB-D_Saliency_Detection_CVPR_2020_paper.pdf)/[Project](https://github.com/OIPLab-DUT/CVPR_SSF-RGBD)

73 | 2020 | A2dele | CVPR          | A2dele: Adaptive and Attentive Depth Distiller for Efficient RGB-D Salient Object Detection | [Paper](https://openaccess.thecvf.com/content_CVPR_2020/papers/Piao_A2dele_Adaptive_and_Attentive_Depth_Distiller_for_Efficient_RGB-D_Salient_CVPR_2020_paper.pdf)/[Project](https://github.com/OIPLab-DUT/CVPR2020-A2dele)

72 | 2020 | JL-DCF | CVPR          | JL-DCF: Joint learning and densely-cooperative fusion framework for RGB-D salient object detection | [Paper](https://openaccess.thecvf.com/content_CVPR_2020/papers/Fu_JL-DCF_Joint_Learning_and_Densely-Cooperative_Fusion_Framework_for_RGB-D_Salient_CVPR_2020_paper.pdf)/[Project](https://github.com/kerenfu/JLDCF)

71 | 2020 | D3Net  |IEEE TNNLS     | Rethinking RGB-D salient object detection: models, datasets, and large-scale benchmarks | [Paper](https://arxiv.org/pdf/1907.06781.pdf)/[Project](https://github.com/DengPingFan/D3NetBenchmark)

70 | 2020 | RGBS   |MTAP           | Salient object detection for RGB-D images by generative adversarial network | [Paper](https://link.springer.com/article/10.1007/s11042-020-09188-8)/Project

69 | 2020 | GFNet   |IEEE SPL       | GFNet: Gate fusion network with res2net for detecting salient objects in RGB-D images | [Paper](https://ieeexplore.ieee.org/document/9090350)/Project

68 | 2020 | SDF   | IEEE TIP       | Improved saliency detection in RGB-D images using two-phase depth estimation and selective deep fusion | [Paper](https://ieeexplore.ieee.org/document/8976428)/Project

67 | 2020 | ICNet | IEEE TIP       | ICNet: Information Conversion Network for RGB-D Based Salient Object Detection| [Paper](https://ieeexplore.ieee.org/document/9024241)/[Project](https://github.com/MathLee/ICNet-for-RGBD-SOD)

66 | 2020 |Triple-Net | IEEE SPL   | Triple-complementary network for RGB-D salient object detection| [Paper](https://ieeexplore.ieee.org/document/9076277)/Project

65 | 2020 |ASIF-Net | IEEE TCYB    | ASIF-Net: Attention steered interweave fusion network for RGB-D salient object detection| [Paper](https://ieeexplore.ieee.org/document/8998588)/[Project](https://github.com/Li-Chongyi/ASIF-Net)

64 | 2020 |BiANet | IEEE TIP       | Bilateral Attention Network for RGB-D Salient Object Detection | [Paper](https://arxiv.org/pdf/2004.14582.pdf)/Project

63 | 2020 |PGHF   | IEEE Access    | Multi-modal weights sharing and hierarchical feature fusion for rgbd salient object detection | [Paper](https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8981965)/Project

62 | 2020 |cmSalGAN | IEEE TMM     | cmSalGAN: RGB-D Salient Object Detection with Cross-View Generative Adversarial Networks | [Paper](https://arxiv.org/pdf/1912.10280.pdf)/Project

61 | 2020 | CoCNN | PR             | CoCNN: RGB-D deep fusion for stereoscopic salient object detection | [Paper](https://www.sciencedirect.com/science/article/abs/pii/S0031320320301321)/Project

60 | 2020 | GFNet | Neurocomputing | A cross-modal adaptive gated fusion generative adversarial network for RGB-D salient object detection | [Paper](https://www.sciencedirect.com/science/article/abs/pii/S0925231220300904)/Project

59 | 2020 | AttNet| IVC            | Attention-guided RGBD saliency detection using appearance information | [Paper](https://www.sciencedirect.com/science/article/abs/pii/S0262885620300202)/Project

58 | 2020 | SSDP   |arXiv          | Synergistic saliency and depth prediction for RGB-D saliency detection | [Paper](https://arxiv.org/pdf/2007.01711.pdf)/Project

57 | 2020 |DPANet | arXiv          | DPANet: Depth Potentiality-Aware Gated Attention Network for RGB-D Salient Object Detection | [Paper](https://arxiv.org/pdf/2003.08608.pdf)/Project

56 | 2019 | DSD   | JVCIR          | Depth-aware saliency detection using convolutional neural networks | [Paper](https://www.sciencedirect.com/science/article/pii/S104732031930118X)/Project

55 | 2019 | DMRA  | ICCV           | Depth-induced Multi-scale Recurrent Attention Network for Saliency Detection | [Paper](https://openaccess.thecvf.com/content_ICCV_2019/papers/Piao_Depth-Induced_Multi-Scale_Recurrent_Attention_Network_for_Saliency_Detection_ICCV_2019_paper.pdf)/[Project](https://github.com/jiwei0921/DMRA)

54 | 2019 | CPFP  | CVPR           | Contrast Prior and Fluid Pyramid Integration for RGBD Salient Object Detection | [Paper](https://openaccess.thecvf.com/content_CVPR_2019/papers/Zhao_Contrast_Prior_and_Fluid_Pyramid_Integration_for_RGBD_Salient_Object_CVPR_2019_paper.pdf)/[Project](https://github.com/JXingZhao/ContrastPrior)

53 | 2019 | EPM   | IEEE Access    | Co-saliency detection for rgbd images based on effective propagation mechanism | [Paper](https://ieeexplore.ieee.org/document/8849990)/Project

52 | 2019 | AFNet | IEEE Access    | Adaptive Fusion for RGB-D Salient Object Detection | [Paper](https://arxiv.org/pdf/1901.01369.pdf)/[Project](https://github.com/Lucia-Ningning/Adaptive_Fusion_RGBD_Saliency_Detection)

51 | 2019 | DGT   | IEEE TCYB      | Going from RGB to RGBD saliency: A depth-guided transformation model | [Paper](https://www.researchgate.net/publication/335360400_Going_From_RGB_to_RGBD_Saliency_A_Depth-Guided_Transformation_Model)/[Project](https://rmcong.github.io/proj_RGBD_sal_DTM_tcyb.html)

50 | 2019 | DCMF  | IEEE TCYB      | Discriminative cross-modal transfer learning and densely cross-level feedback fusion for RGB-D salient object detection | [Paper](https://ieeexplore.ieee.org/document/8820129)/Project

49 | 2019 | TANet | IEEE TIP       | Three-stream attention-aware network for RGB-D salient object detection | [Paper](https://ieeexplore.ieee.org/document/8603756)/Project

48 | 2019 | MMCI  | PR             | Multi-modal fusion network with multi-scale multi-path and cross-modal interactions| [Paper](https://www.sciencedirect.com/science/article/abs/pii/S0031320318303054)/Project

47 | 2019 | PDNet | ICME           | Prior-model guided depth-enhanced network for salient object detection| [Paper](https://arxiv.org/pdf/1803.08636.pdf)/[Project](https://github.com/cai199626/PDNet)

46 | 2019 | CAFM  | IEEE TSMC      | Global and Local-Contrast Guides Content-Aware Fusion for RGB-D Saliency Prediction | [Paper](https://ieeexplore.ieee.org/document/8941002)/Project

45 | 2019 | DIL   | MTAP           | Salient object segmentation based on depth-aware image layering | [Paper](https://link.springer.com/article/10.1007/s11042-018-6736-4)/Project

44 | 2019 | TSRN  | ICIP           | Two-stream refinement network for RGB-D saliency detection | [Paper](https://ieeexplore.ieee.org/document/8803653)/Project

43 | 2019 | MLF   | SPL            | RGB-D salient object detection by a CNN with multiple layers fusion | [Paper](https://ieeexplore.ieee.org/document/8638984)/Project

42 | 2019 | SSRC  | Neurocomputing | Salient object detection for RGB-D image by single stream recurrent convolution neural network | [Paper](https://www.sciencedirect.com/science/article/abs/pii/S0925231219309403)/Project

41 | 2018 | CDB   | Neurocomputing | Stereoscopic saliency model using contrast and depth-guided-background prior | [Paper](https://www.sciencedirect.com/science/article/abs/pii/S0925231217317034)/Project

40 | 2018 | ACCF  | IROS           | Attention-Aware Cross-Modal Cross-Level Fusion Network for RGB-D Salient Object Detection | [Paper](https://ieeexplore.ieee.org/document/8594373)/Project

39 | 2018 | SCDL  | ICDSP          | Rgbd salient object detection using spatially coherent deep learning framework | [Paper](https://ieeexplore.ieee.org/document/8631584)/Project

38 | 2018 | PCF   | CVPR           | Progressively complementarityaware fusion network for RGB-D salient object detection | [Paper](https://openaccess.thecvf.com/content_cvpr_2018/papers/Chen_Progressively_Complementarity-Aware_Fusion_CVPR_2018_paper.pdf)/[Project](https://github.com/haochen593/PCA-Fuse_RGBD_CVPR18)

37 | 2018 | CTMF  | IEEE TCYB      | CNNs-based RGB-D saliency detection via cross-view transfer and multiview fusion | [Paper](https://ieeexplore.ieee.org/document/8091125)/[Project](https://github.com/haochen593/CTMF)

36 | 2018 | ICS   | IEEE TIP       | Co-saliency detection for RGBD images based on multi-constraint feature matching and cross label propagation | [Paper](https://arxiv.org/pdf/1710.05172.pdf)/Project

35 | 2018 | HSCS  | IEEE TMM       | HSCS: Hierarchical sparsity based co-saliencydetection for RGBD images | [Paper](https://arxiv.org/pdf/1811.06679.pdf)/[Project](https://github.com/rmcong/Results-for-2018TMM-HSCS)

34 | 2017 | ISC   | SIVP           | An integration of bottom-up and top-down salient cueson rgb-d data: saliency from objectness versus non-objectness | [Paper](https://arxiv.org/pdf/1807.01532.pdf)/Project

33 | 2017 | MCLP  | IEEE TCYB      | An iterative co-saliency framework for RGBD images | [Paper](https://arxiv.org/pdf/1711.01371.pdf)/Project

32 | 2017 | DF    | IEEE TIP       | RGBD Salient Object Detection via Deep Fusion | [Paper](https://arxiv.org/pdf/1607.03333.pdf)/[Project](https://pan.baidu.com/s/1Y-PqAjuH9xREBjfl7H45HA)

31 | 2017 | MDSF  | IEEE TIP       | Depth-Aware Salient Object Detection and Segmentation via Multiscale Discriminative Saliency Fusion and Bootstrap Learning | [Paper](https://ieeexplore.ieee.org/document/7938352)/[Project](https://github.com/ivpshu/Depth-aware-salient-object-detection-and-segmentation-via-multiscale-discriminative-saliency-fusion-)

30 | 2017 | MFF   | IEEE SPL       | RGB-D saliency object detection via minimum barrier distance transformand saliency fusion | [Paper](https://wanganzhi.github.io/papers/SPL17.pdf)/Project

29 | 2017 | TPF   | ICCVW          | A Three-Pathway Psychobiological Framework of Salient Object Detection Using Stereoscopic Technology | [Paper](https://ieeexplore.ieee.org/document/8265566)/Project

28 | 2017 | CDCP  | ICCVW          | An innovative salient object detection using center-dark channel prior | [Paper](https://openaccess.thecvf.com/content_ICCV_2017_workshops/papers/w22/Zhu_An_Innovative_Salient_ICCV_2017_paper.pdf)/[Project](https://github.com/ChunbiaoZhu/ACVR2017)

27 | 2017 | BED   | ICCVW          | Learning RGB-D Salient Object Detection using background enclosure, depth contrast, and top-down features | [Paper](https://openaccess.thecvf.com/content_ICCV_2017_workshops/papers/w40/Shigematsu_Learning_RGB-D_Salient_ICCV_2017_paper.pdf)/[Project](https://github.com/sshige/rgbd-saliency)

26 | 2017 | MFLN  | ICCVS          | RGB-D Saliency Detection by Multi-stream Late Fusion Network | [Paper](https://link.springer.com/chapter/10.1007/978-3-319-68345-4_41)/Project

25 | 2017 | M3Net | IROS           | M3Net: Multi-scale multi-path multi-modal fusion network and example application to RGB-D salient object detection | [Paper](https://ieeexplore.ieee.org/abstract/document/8206370)/Project

24 | 2017 | HOSO  | DICTA          | HOSO: Histogram of Surface Orientation for RGB-D Salient Object Detection | [Paper](https://ieeexplore.ieee.org/document/8227440)/Project

23 | 2016 | GM    | ACCV           | Visual Saliency detection for RGB-D images with generative mode | [Paper](https://link.springer.com/chapter/10.1007/978-3-319-54193-8_2)/Project

22 | 2016 | DSF   | ICASSP         | Depth-aware saliency detection using discriminative saliency fusion | [Paper](https://ieeexplore.ieee.org/document/7471952)/Project

21 | 2016 | DCI   | ICASSP         | Saliency analysis based on depth contrast increased | [Paper](http://sites.nlsde.buaa.edu.cn/~shenghao/Download/publications/2016/9.Saliency%20analysis%20based%20on%20depth%20contrast%20increased.pdf)/Project

20 | 2016 | BF    | ICPR           | RGB-D saliency detection under Bayesian framework | [Paper](https://ieeexplore.ieee.org/document/7899911)/Project

19 | 2016 | DCMC  | IEEE SPL       | Saliency detection for stereoscopic images based on depth confidence analysis and multiple cues fusion  | [Paper](https://ieeexplore.ieee.org/document/7457641)/[Project](https://github.com/rmcong/Code-for-DCMC-method)

18 | 2016 | SE    | ICME           | Salient object detection for RGB-D image via saliency evolution | [Paper](https://ieeexplore.ieee.org/document/7552907)/Project

17 | 2016 | LBE   | CVPR           | Local Background Enclosure for RGB-D Salient Object Detection| [Paper](https://www.cv-foundation.org/openaccess/content_cvpr_2016/app/S10-09.pdf)/[Project](http://users.cecs.anu.edu.au/~u4673113/lbe.html)

16 | 2016 | PRC   | IEEE Access    | Improving RGBD Saliency Detection Using Progressive Region Classification and Saliency Fusion| [Paper](https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=7762806)/Project

15 | 2015 | SF    | CAC            | Selective features for RGB-D saliency | [Paper](https://ieeexplore.ieee.org/document/7382554)/Project

14 | 2015 | MGMR  | ICIP           | RGB-D saliency detection via mutual guided manifold ranking | [Paper](https://ieeexplore.ieee.org/document/7350882)/Project

13 | 2015 | SRD   | ICRA           | Salient Regions Detection for Indoor Robots using RGB-D Data | [Paper](http://www.cogsys.cs.uni-tuebingen.de/publikationen/2015/Jiang_ICRA15.pdf)/Project

12 | 2015 | DIC   | TVC            | Depth incorporating with color improves salient object detection | [Paper](https://link.springer.com/article/10.1007/s00371-014-1059-6)/Project

11 | 2015 | SFP   | ICIMCS         | Salient object detection in RGB-D image based on saliency fusion and propagation | [Paper](https://dl.acm.org/doi/10.1145/2808492.2808551)/Project

10 | 2015 | GP    | CVPRW          | Exploiting global priors for RGB-D saliency detection | [Paper](https://www.cv-foundation.org/openaccess/content_cvpr_workshops_2015/W14/papers/Ren_Exploiting_Global_Priors_2015_CVPR_paper.pdf)/[Project](https://github.com/JianqiangRen/Global_Priors_RGBD_Saliency_Detection)

09 | 2014 | ACSD  | ICIP           | Depth saliency based on anisotropic center-surround difference | [Paper](https://projet.liris.cnrs.fr/imagine/pub/proceedings/ICIP-2014/Papers/1569913831.pdf)/[Project](https://github.com/HzFu/DES_code)

08 | 2014 | DESM  | ICIMCS         | Depth Enhanced Saliency Detection Method | [Paper](http://dpfan.net/wp-content/uploads/DES_dataset_ICIMCS14.pdf)/Project

07 | 2014 | LHM   | ECCV           | RGBD Salient Object Detection: A Benchmark and Algorithms | [Paper](http://dpfan.net/wp-content/uploads/NLPR_dataset_ECCV14.pdf)/[Project](https://sites.google.com/site/rgbdsaliency/code)

06 | 2014 | SRDS  | ICDSP          | Salient region detection for stereoscopic images | [Paper](https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6900706)/Project 

05 | 2013 | SOS   | Neurocomputing | Depth really Matters: Improving Visual Salient Region Detection with Depth | [Paper](https://www.sciencedirect.com/science/article/abs/pii/S0925231213002981)/Project 

04 | 2013 | RC    | BMVC           | Depth really Matters: Improving Visual Salient Region Detection with Depth | [Paper](http://cdn.iiit.ac.in/cdn/cvit.iiit.ac.in/images/ConferencePapers/2013/cv_deepth-really.pdf)/Project 

03 | 2013 | LS    | BMVC           | An In Depth View of Saliency                                     | [Paper](http://www.cs.utah.edu/~thermans/papers/ciptadi-bmvc2013.pdf)/Project 

02 | 2012 | RCM   | ICCSE          | Depth combined saliency detection based on region contrast model | [Paper](https://ieeexplore.ieee.org/document/6295184)/Project 

01 | 2012 | DM    | ECCV           | Depth matters: Influence of depth cues on visual saliency        | [Paper](https://link.springer.com/content/pdf/10.1007/978-3-642-33709-3_8.pdf)/Project 

------

------

## RGB-D SOD Datasets:    

**No.** |**Dataset** | **Year** | **Pub.** |**Size** | **#Obj.** | **Types** | **Resolution** | **Download**

:-: | :-: | :-: | :-  | :-  | :-:| :-: | :-: | :-:

1   | [**STERE**](http://dpfan.net/wp-content/uploads/STERE_dataset_CVPR12.pdf)   |2012 |CVPR   | 1000 | ~One       |Internet             | [251-1200] * [222-900] | [link](https://github.com/DengPingFan/D3NetBenchmark/)

2   | [**GIT**](http://www.bmva.org/bmvc/2013/Papers/paper0112/abstract0112.pdf)  |2013 |BMVC   | 80   | Multiple  |Home environment     | 640 * 480 | [link](https://github.com/DengPingFan/D3NetBenchmark/)

3   | [**DES**](http://dpfan.net/wp-content/uploads/DES_dataset_ICIMCS14.pdf)     |2014 |ICIMCS | 135  | One       |Indoor               | 640 * 480 | [link](https://github.com/DengPingFan/D3NetBenchmark/)

4   | [**NLPR**](http://dpfan.net/wp-content/uploads/NLPR_dataset_ECCV14.pdf)     |2014 |ECCV   | 1000 | Multiple  |Indoor/outdoor       | 640 * 480, 480 * 640 | [link](https://github.com/DengPingFan/D3NetBenchmark/)

5   | [**LFSD**](http://dpfan.net/wp-content/uploads/LFSD_dataset_CVPR14.pdf)     |2014 |CVPR   | 100  | One       |Indoor/outdoor       | 360 * 360 | [link](https://github.com/DengPingFan/D3NetBenchmark/)

6   | [**NJUD**](http://dpfan.net/wp-content/uploads/NJU2K_dataset_ICIP14.pdf)     |2014 |ICIP   | 1985 | ~One       |Moive/internet/photo | [231-1213] * [274-828] | [link](https://github.com/DengPingFan/D3NetBenchmark/)

7   | [**SSD**](http://dpfan.net/wp-content/uploads/SSD_dataset_ICCVW17.pdf)      |2017 |ICCVW  | 80   | Multiple  |Movies               | 960 *1080  | [link](https://github.com/DengPingFan/D3NetBenchmark/)

8   | [**DUT-RGBD**](https://openaccess.thecvf.com/content_ICCV_2019/papers/Piao_Depth-Induced_Multi-Scale_Recurrent_Attention_Network_for_Saliency_Detection_ICCV_2019_paper.pdf) |2019 |ICCV   | 1200 | Multiple  |Indoor/outdoor       | 400 * 600 | [link](https://github.com/DengPingFan/D3NetBenchmark/)

9   | [**SIP**](http://dpfan.net/wp-content/uploads/SIP_dataset_TNNLS20.pdf)     |2020 |TNNLS  | 929  | Multiple  |Person in wild       | 992 * 774 | [link](https://github.com/DengPingFan/D3NetBenchmark/)

10  | [**ReDWeb-S**](https://arxiv.org/pdf/2010.05537.pdf)     |2020 |arXiv  | 3179   | Multiple  |Diversity       | [133-937] * [132-996] | [link](https://github.com/nnizhang/SMAC)

11  | [**COME15K**](https://arxiv.org/pdf/2010.05537.pdf)      |2021 |ICCV  | 15K   | Multiple  |Diversity       | -- | [link](https://github.com/JingZhang617/cascaded_rgbd_sod)

------

------

## Light Field SOD:    

### LF SOD Models:  

**No.** | **Year** | **Model** |**Pub.** | **Title** | **Links** 

:-: | :-: | :-: | :-  | :-  | :-: 

01 | 2014 | LFS   | CVPR   | Saliency detection on light field | [Paper](https://sites.duke.edu/nianyi/files/2020/06/Li_Saliency_Detection_on_2014_CVPR_paper.pdf)/[Project](https://sites.duke.edu/nianyi/publication/saliency-detection-on-light-field/)

02 | 2015 | WSC   | CVPR   | A weighted sparse coding framework for saliency detection | [Paper](https://openaccess.thecvf.com/content_cvpr_2015/papers/Li_A_Weighted_Sparse_2015_CVPR_paper.pdf)/[Project](https://www.researchgate.net/publication/294874666_Code_WSC)

03 | 2015 | DILF  | IJCAI  | Saliency detection with a deeper investigationof light field | [Paper](https://www.ijcai.org/Proceedings/15/Papers/313.pdf)/[Project](https://github.com/pencilzhang/lightfieldsaliency_ijcai15)

04 | 2016 | RL    | ICASSP | Relative location for light field saliency detection | [Paper](http://sites.nlsde.buaa.edu.cn/~shenghao/Download/publications/2016/11.Relative%20location%20for%20light%20field%20saliency%20detection.pdf)/Project

05 | 2017 | MA    | ACM TOMM   | Saliency detection on light field: A multi-cue approach | [Paper](http://www.linliang.net/wp-content/uploads/2017/07/ACMTOM_Saliency.pdf)/Project

06 | 2017 | BIF   | NPL    | A two-stage bayesian integration framework for salient object detection on light field | [Paper](https://link.springer.com/article/10.1007/s11063-017-9610-x)/Project

07 | 2017 | LFS   | IEEE TPAMI  | Saliency Detection on Light Field | [Paper](https://ieeexplore.ieee.org/document/7570181)/[Project](https://sites.duke.edu/nianyi/publication/saliency-detection-on-light-field/)

08 | 2017 | RLM   | ICIVC  | Saliency detection with relative location measure in light field image | [Paper](https://ieeexplore.ieee.org/document/7984449/)/Project

09 | 2017 | SGDC  | CVPR   | Salience guided depth calibration for perceptually optimized compressive light field 3D display | [Paper](https://ieeexplore.ieee.org/document/8578315/)/Project

10 | 2018 | DCA   | FiO    | Depth-induced cellular automata for light field saliency | [Paper](https://ieeexplore.ieee.org/document/8866752)/Project

11 | 2019 | DLLF  | ICCV   | Deep learning for light field saliency detection | [Paper](https://openaccess.thecvf.com/content_ICCV_2019/papers/Wang_Deep_Learning_for_Light_Field_Saliency_Detection_ICCV_2019_paper.pdf)/[Project](https://github.com/OIPLab-DUT/ICCV2019_Deeplightfield_Saliency)

12 | 2019 | DLSD  | IJCAI  | Deep light-field-driven saliency detection from a single view | [Paper](https://www.ijcai.org/Proceedings/2019/0127.pdf)/Project

13 | 2019 | Molf  | NIPS   | Memory-oriented decoder for light field salient object detection | [Paper](https://papers.nips.cc/paper/8376-memory-oriented-decoder-for-light-field-salient-object-detection.pdf)/[Project](https://github.com/OIPLab-DUT/MoLF)

14 | 2020 | ERNet | AAAI   | Exploit and replace: An asymmetrical two-stream architecture for versatile light field saliency detection | [Paper](https://www.aiide.org/ojs/index.php/AAAI/article/view/6860)/Project

15 | 2020 | DCA   | IEEE TIP    | Saliency detection via depth-induced cellular automata onlight field | [Paper](https://ieeexplore.ieee.org/document/8866752)/Project

16 | 2020 | RDFD  | MTAP   | Region-based depth feature descriptor for saliency detection light field | [Paper](https://link.springer.com/article/10.1007%2Fs11042-020-08890-x)/Project

17 | 2020 | LFNet | IEEE TIP    | LFNet light field fusion network for salient object detection | [Paper](https://ieeexplore.ieee.org/document/9082882)/[Project](https://github.com/jiwei0921/LFNet)

18 | 2020 | LFDCN | IEEE TIP    | Light field saliency detection with deep convolutional networks | [Paper](https://arxiv.org/pdf/1906.08331.pdf)/[Project](https://github.com/pencilzhang/MAC-light-field-saliency-net)

### LF Datasets:  

**No.** |**Dataset** | **Year** | **Pub.** |**Size** | **Description** | **Download**

:-: | :-: | :-: | :-  | :-  | :-:| :-: 

1   | [**LFSD**](https://sites.duke.edu/nianyi/files/2020/06/Li_Saliency_Detection_on_2014_CVPR_paper.pdf)   |2014 |CVPR   | 100  | It contains 60 indoor and 40 outdoor scenes, and most scenes consist of only one salient object | [link](https://sites.duke.edu/nianyi/publication/saliency-detection-on-light-field/)

2   | [**HFUT**](http://www.linliang.net/wp-content/uploads/2017/07/ACMTOM_Saliency.pdf)                 |2017 |ACM TOMM   | 255  | Most scenes contain multipleobjects that appear within different locations and scales under complex background clutter | [link](https://github.com/pencilzhang/HFUT-Lytro-dataset)

3   | [**HFUT**](http://www.linliang.net/wp-content/uploads/2017/07/ACMTOM_Saliency.pdf)                 |2017 |ACM TOMM   | 255  | Most scenes contain multipleobjects that appear within different locations and scales under complex background clutter | [link](https://github.com/pencilzhang/HFUT-Lytro-dataset)

4   | [**DUTLF-FS**](https://openaccess.thecvf.com/content_ICCV_2019/papers/Wang_Deep_Learning_for_Light_Field_Saliency_Detection_ICCV_2019_paper.pdf)             |2019 |ICCV       | 1465 | It contains several challenges, including lower contrast between salient objects and cluttered background, multiple disconnected salient objects, and dark or strong light conditions | [link](https://github.com/OIPLab-DUT/ICCV2019_Deeplightfield_Saliency)

5   | [**DUTLF-MV**](https://www.ijcai.org/Proceedings/2019/0127.pdf)                                    |2019 |IJCAI      | 1580 | Each light field consists of multi-view images and a corresponding ground truth | [link](https://github.com/TuesdayT/IJCAI2019-Deep-Light-Field-Driven-Saliency-Detection-from-A-Single-View)

6   | [**Lytro Illum**](https://arxiv.org/pdf/1906.08331.pdf)                                            |2020 |IEEE TIP   | 640  | It includes several challenging factors, e.g., inconsistent illumi?nation conditions, and small salient objects existing in a similar or cluttered background | [link](https://github.com/pencilzhang/MAC-light-field-saliency-net)

------

------

## Evaluation:    

### Overall Evaluation:  

![alt text](./figures/Fig_overall.jpg)

*Fig.1: A comprehensive evaluation for 24 representative RGB-D based SOD models, including [LHM](http://dpfan.net/wp-content/uploads/NLPR_dataset_ECCV14.pdf), [ACSD](http://dpfan.net/wp-content/uploads/DES_dataset_ICIMCS14.pdf), [DESM](http://dpfan.net/wp-content/uploads/DES_dataset_ICIMCS14.pdf), 

[GP](https://www.cv-foundation.org/openaccess/content_cvpr_workshops_2015/W14/papers/Ren_Exploiting_Global_Priors_2015_CVPR_paper.pdf),

[LBE](https://www.cv-foundation.org/openaccess/content_cvpr_2016/app/S10-09.pdf), 

[DCMC](https://ieeexplore.ieee.org/document/7457641), 

[SE](https://ieeexplore.ieee.org/document/7552907), 

[CDCP](https://openaccess.thecvf.com/content_ICCV_2017_workshops/papers/w22/Zhu_An_Innovative_Salient_ICCV_2017_paper.pdf), 

[CDB](https://www.sciencedirect.com/science/article/abs/pii/S0925231217317034), 

[DF](https://arxiv.org/pdf/1607.03333.pdf), 

[PCF](https://openaccess.thecvf.com/content_cvpr_2018/papers/Chen_Progressively_Complementarity-Aware_Fusion_CVPR_2018_paper.pdf), 

[CTMF](https://ieeexplore.ieee.org/document/8091125), 

[CPFP](https://openaccess.thecvf.com/content_CVPR_2019/papers/Zhao_Contrast_Prior_and_Fluid_Pyramid_Integration_for_RGBD_Salient_Object_CVPR_2019_paper.pdf), 

[TANet](https://ieeexplore.ieee.org/document/8603756), 

[AFNet](https://arxiv.org/pdf/1901.01369.pdf), 

[MMCI](https://www.sciencedirect.com/science/article/abs/pii/S0031320318303054), 

[DMRA](https://openaccess.thecvf.com/content_ICCV_2019/papers/Piao_Depth-Induced_Multi-Scale_Recurrent_Attention_Network_for_Saliency_Detection_ICCV_2019_paper.pdf), 

[D3Net](https://arxiv.org/pdf/1907.06781.pdf), 

[SSF](https://openaccess.thecvf.com/content_CVPR_2020/papers/Zhang_Select_Supplement_and_Focus_for_RGB-D_Saliency_Detection_CVPR_2020_paper.pdf), 

[A2dele](https://openaccess.thecvf.com/content_CVPR_2020/papers/Piao_A2dele_Adaptive_and_Attentive_Depth_Distiller_for_Efficient_RGB-D_Salient_CVPR_2020_paper.pdf), 

[S2MA](https://openaccess.thecvf.com/content_CVPR_2020/papers/Liu_Learning_Selective_Self-Mutual_Attention_for_RGB-D_Saliency_Detection_CVPR_2020_paper.pdf), 

[ICNet](https://ieeexplore.ieee.org/document/9024241), 

[JL-DCF](https://openaccess.thecvf.com/content_CVPR_2020/papers/Fu_JL-DCF_Joint_Learning_and_Densely-Cooperative_Fusion_Framework_for_RGB-D_Salient_CVPR_2020_paper.pdf), and 

[UC-Net](https://openaccess.thecvf.com/content_CVPR_2020/papers/Zhang_UC-Net_Uncertainty_Inspired_RGB-D_Saliency_Detection_via_Conditional_Variational_Autoencoders_CVPR_2020_paper.pdf). 

We obtain the terms of $S_{\alpha}$ and MAE values for the 24 models on five datasets (i.e., [STERE](http://dpfan.net/wp-content/uploads/STERE_dataset_CVPR12.pdf), 

[NLPR](http://dpfan.net/wp-content/uploads/NLPR_dataset_ECCV14.pdf), 

[LFSD](http://dpfan.net/wp-content/uploads/LFSD_dataset_CVPR14.pdf), 

[DES](http://dpfan.net/wp-content/uploads/DES_dataset_ICIMCS14.pdf), and [SIP](http://dpfan.net/wp-content/uploads/SIP_dataset_TNNLS20.pdf)

). We report the mean values of $S_{\alpha}$ and MAE across the five datasets. Note that these better models are shown in the upper left corner (\ie, with a larger $S_{\alpha}$ and smaller MAE).*

======================= **run evaluation code** ===============================

1. We have computed values of different evaluation metrics for each image of each models and save as '***.mat', and the results can be downloaded from Google Drive or [Baidu Drive](https://pan.baidu.com/s/1kGRoErBvEzYY3t4pRxUSrA)(extraction code: urra).

2. Please unzip the downloaded file 'Sal_Det_Results_24_Models.zip' and put it into the file 'results';

3. To run 'run_overall_evaluation.m' (plot Fig.1 )

======================================================================

![alt text](./figures/Fig_PR.jpg)

*Fig.2: PR curves for 24 RGB-D based models on [STERE](http://dpfan.net/wp-content/uploads/STERE_dataset_CVPR12.pdf),

[NLPR](http://dpfan.net/wp-content/uploads/NLPR_dataset_ECCV14.pdf), 

[LFSD](http://dpfan.net/wp-content/uploads/LFSD_dataset_CVPR14.pdf), 

[DES](http://dpfan.net/wp-content/uploads/DES_dataset_ICIMCS14.pdf), 

[SIP](http://dpfan.net/wp-content/uploads/SIP_dataset_TNNLS20.pdf), 

[GIT](http://www.bmva.org/bmvc/2013/Papers/paper0112/abstract0112.pdf), 

[SSD](http://dpfan.net/wp-content/uploads/SSD_dataset_ICCVW17.pdf), and 

[NJUD](http://dpfan.net/wp-content/uploads/NJU2K_dataset_ICIP14.pdf) datasets.*

![alt text](./figures/Fig_F_curve.jpg)

*Fig.3:  F-measures under different thresholds for 24 RGB-D based models on [STERE](http://dpfan.net/wp-content/uploads/STERE_dataset_CVPR12.pdf),

[NLPR](http://dpfan.net/wp-content/uploads/NLPR_dataset_ECCV14.pdf), 

[LFSD](http://dpfan.net/wp-content/uploads/LFSD_dataset_CVPR14.pdf), 

[DES](http://dpfan.net/wp-content/uploads/DES_dataset_ICIMCS14.pdf), 

[SIP](http://dpfan.net/wp-content/uploads/SIP_dataset_TNNLS20.pdf), 

[GIT](http://www.bmva.org/bmvc/2013/Papers/paper0112/abstract0112.pdf), 

[SSD](http://dpfan.net/wp-content/uploads/SSD_dataset_ICCVW17.pdf), and 

[NJUD](http://dpfan.net/wp-content/uploads/NJU2K_dataset_ICIP14.pdf) datasets.*

======================= **run plot code** ===========================

1. To run 'run_plot_curves.m' (plot Fig.2 and Fig.3)

=============================================================

------

------

### Attribute-based Evaluation:  

1. Downloading attribute datasets [Baidu Drive](https://pan.baidu.com/s/11CZ7njZ2X9CTKN8nGFlAog) (Code: tktz) or [Google Drive](https://drive.google.com/file/d/1DQ7y1I27FbkE4AA30gO4uInVWGgnz3A_/view?usp=sharing).

    

### RGB-D SOD Benchmark :  

The complete RGB-D SOD benchmark can be found in this page:

[https://mmcheng.net/socbenchmark/](https://mmcheng.net/socbenchmark/)

------

------

### Citation:  

If you find our survey paper and evaluation code are useful, please cite the following paper:

	@article{zhou2020rgbd,

  		title={RGB-D Salient Object Detection: A Survey},

  		author={Zhou, Tao and Fan, Deng-Ping and Cheng, Ming-Ming and Shen, Jianbing and Shao, Ling},

  		journal={Computational Visual Media},

  		pages={1--33},

  		year={2021},

  		publisher={Springer}

	}