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Review Article | Open Access

RGB-D salient object detection: A survey

Tao Zhou1, Deng-Ping Fan1( ), Ming-Ming Cheng2, Jianbing Shen1, Ling Shao1
Inception Institute of Artificial Intelligence (IIAI), Abu Dhabi,United Arab Emirates
CS, Nankai University, Tianjin 300350, China
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Abstract

Salient object detection, which simulateshuman visual perception in locating the most significant object(s) in a scene, has been widely applied to various computer vision tasks. Now, the advent of depth sensors means that depth maps can easily be captured; this additional spatial information can boost the performance of salient object detection. Although various RGB-D based salient object detection models with promising performance have been proposed over the past several years, an in-depth understanding of these models and the challenges in this field remains lacking. In this paper, we provide a comprehensive survey of RGB-D based salient object detection models from various perspectives, and review related benchmark datasets in detail. Further, as light fields can also provide depth maps, we review salient object detection models and popular benchmark datasets from this domain too. Moreover, to investigate the ability of existing models to detect salient objects, we have carried out a comprehensive attribute-based evaluation of several representative RGB-D based salient object detection models. Finally, we discuss several challenges and open directions of RGB-D based salient object detection for future research. All collected models, benchmark datasets, datasets constructed for attribute-based evaluation, and related code are publicly available at https://github.com/taozh2017/RGBD-SODsurvey.

Keywords

RGB-Dsaliencylight fieldsbenchmarks

References

[1]
Fan, D. P.; Cheng, M. M.; Liu, J. J.; Gao, S. H.; Hou, Q. B.; Borji, A. Salient objects in clutter: Bringing salient object detection to the foreground. In: Computer Vision - ECCV 2018. Lecture Notes in Computer Science, Vol. 11219. Ferrari, V.; Hebert, M.; Sminchisescu, C.; Weiss, Y. Eds. Springer Cham, 196-212, 2018.
[2]
Nie, G.-Y.; Cheng, M.-M.; Liu, Y.; Liang, Z.; Fan, D.-P.; Liu, Y.; Wang, Y. Multi-level context ultra-aggregation for stereo matching. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 3278-3286, 2019.
Crossref
[3]
Zhu, J. Y.; Wu, J. J.; Xu, Y.; Chang, E., Tu, Z. W. Unsupervised object class discovery via saliency-guided multiple class learning. IEEE Transactions on Pattern Analysis and Machine Intelligence Vol. 37, No. 4, 862-875, 2015.
Crossref Google Scholar
[4]
Fan, D. P.; Li, T. P.; Lin, Z.; Ji, G. P.; Zhang, D. W.; Cheng, M. M.; Fu, H.; Shen, J. Re-thinking co-salient object detection. arXiv preprint arXiv:2007.03380, 2020.
Google Scholar
[5]
Rapantzikos, K.; Avrithis, Y.; Kollias, S. Dense saliency-based spatiotemporal feature points for action recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 1454-1461, 2009.
Crossref
[6]
Fan, D.-P.; Wang, W.; Cheng, M.-M.; Shen, J. Shifting more attention to video salient object detection. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 8554-8564, 2019.
Crossref
[7]
Wang, W. G.; Shen, J. B.; Yang, R. G.; Porikli, F. Saliency-aware video object segmentation. IEEE Transactions on Pattern Analysis and Machine Intelligence Vol. 40, No. 1, 20-33, 2018.
Crossref Google Scholar
[8]
Song, H. M.; Wang, W. G.; Zhao, S. Y.; Shen, J. B.; Lam, K. M. Pyramid dilated deeper ConvLSTM for video salient object detection. In: Computer Vision - ECCV 2018. Lecture Notes in Computer Science, Vol. 11215. Ferrari, V.; Hebert, M.; Sminchisescu, C.; Weiss, Y. Eds. Springer Cham, 744-760, 2018.
[9]
Wang, W. G.; Shen, J. B.; Shao, L. Video salient object detection via fully convolutional networks. IEEE Transactions on Image Processing Vol. 27, No. 1, 38-49, 2018.
Crossref Google Scholar
[10]
Shimoda, W.; Yanai, K. Distinct class-specific saliency maps for weakly supervised semantic segmentation. In: Computer Vision - ECCV 2016. Lecture Notes in Computer Science, Vol. 9908. Leibe, B.; Matas, J.; Sebe, N.; Welling, M. Eds. Springer Cham, 218-234, 2016.
[11]
Zeng, Y.; Zhuge, Y.; Lu, H.; Zhang, L. Joint learning of saliency detection and weakly supervised semantic segmentation. In: Proceedings of the IEEE International Conference on Computer Vision, 7223-7233, 2019.
[12]
Fan, D. P.; Ji, G. P.; Zhou, T.; Chen, G.; Fu, H. Z.; Shen, J. B.; Shao, L. PraNet: Parallel reverse attention network for polyp segmentation. In: Medical Image Computing and Computer Assisted Intervention - MICCAI 2020. Lecture Notes in Computer Science, Vol. 12266. Martel, A. L. et al. Eds. Springer Cham, 263-273, 2020.
Crossref
[13]
Fan, D. P.; Zhou, T.; Ji, G. P.; Zhou, Y.; Chen, G.; Fu, H. Z.; Shen, J.; Shao, L. Inf-Net: Automatic COVID-19 lung infection segmentation from CT images. IEEE Transactions on Medical Imaging Vol. 39, No. 8, 2626-2637, 2020.
Crossref Google Scholar
[14]
Wu, Y.-H.; Gao, S.-H.; Mei, J.; Xu, J.; Fan, D.-P.; Zhao, C.- W.; Cheng, M.-M. JCS: An explainable COVID-19 diagnosis system by joint classification and segmentation. arXiv preprint arXiv:2004.07054, 2020.
Google Scholar
[15]
Mahadevan, V.; Vasconcelos, N. Saliency-based discriminant tracking. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 1007-1013, 2009.
Crossref
[16]
Hong, S.; You, T.; Kwak, S.; Han, B. Online tracking by learning discriminative saliency map with convolutional neural network. In: Proceedings of the International Conference on Machine Learning, 597-606, 2015.
[17]
Zhao, R.; Oyang, W.; Wang, X. G. Person re-identification by saliency learning. IEEE Transactions on Pattern Analysis and Machine Intelligence Vol. 39, No. 2, 356-370, 2017.
Crossref Google Scholar
[18]
Martinel, N., Micheloni, C., Foresti, G. L. Kernelized saliency-based person Re-identification through multiple metric learning. IEEE Transactions on Image Processing Vol. 24, No. 12, 5645-5658, 2015.
Crossref Google Scholar
[19]
Fan, D.-P.; Ji, G.-P.; Sun, G.; Cheng, M.-M.; Shen, J.; Shao, L. Camouaged object detection. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2777-2787, 2020.
Crossref
[20]
Liu, G.; Fan, D. A model of visual attention for natural image retrieval. In: Proceedings of the IEEE Conference on Information Science and Cloud Computing Companion, 728-733, 2013.
Crossref
[21]
Zhao, J.-X.; Liu, J.-J.; Fan, D.-P.; Cao, Y.; Yang, J.; Cheng, M.-M. EGNet: Edge guidance network for salient object detection. In: Proceedings of the IEEE International Conference on Computer Vision, 8779-8788, 2019.
Crossref
[22]
Tu, W.-C.; He, S.; Yang, Q.; Chien, S.-Y. Real-time salient object detection with a minimum spanning tree. In: Proceedings of the IEEE conference on Computer Vision and Pattern Recognition, 2334-2342, 2016.
[23]
Xia, C.; Li, J.; Chen, X.; Zheng, A.; Zhang, Y. What is and what is not a salient object? Learning salient object detector by ensembling linear exemplar regressors. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 4142-4150, 2017.
Crossref
[24]
Hou, X.; Zhang, L. Saliency detection: A spectral residual approach. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 1-8, 2007.
Crossref
[25]
Yan, Q.; Xu, L.; Shi, J.; Jia, J. Hierarchical saliency detection. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 1155-1162, 2013.
Crossref
[26]
Yang, C.; Zhang, L.; Lu, H.; Ruan, X.; Yang, M.-H. Saliency detection via graph-based manifold ranking. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 3166-3173, 2013.
Crossref
[27]
Li, G.; Yu, Y. Deep contrast learning for salient object detection. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 478-487, 2016.
Crossref
[28]
Zhang, D. W.; Meng, D. Y.; Han, J. W. Co-saliency detection via a self-paced multiple-instance learning framework. IEEE Transactions on Pattern Analysisand Machine Intelligence Vol. 39, No. 5, 865-878, 2017.
Crossref Google Scholar
[29]
Zhang, P.; Wang, D.; Lu, H.; Wang, H.; Ruan, X. Amulet: Aggregating multi-level convolutional features for salient object detection. In: Proceedings of the IEEE International Conference on Computer Vision, 202-211, 2017.
Crossref
[30]
Zhang, P.; Wang, D.; Lu, H.; Wang, H.; Yin, B. Learning uncertain convolutional features for accurate saliency detection. In: Proceedings of the IEEE International Conference on Computer Vision, 212-221, 2017.
Crossref
[31]
Wang, T.; Borji, A.; Zhang, L.; Zhang, P.; Lu, H. A stagewise refinement model for detecting salient objects in images. In: Proceedings of the IEEE International Conference on Computer Vision, 4019-4028, 2017.
Crossref
[32]
Li, X.; Yang, F.; Cheng, H.; Liu, W.; Shen, D. G. Contour knowledge transfer for salient object detection. In: Computer Vision - ECCV 2018. Lecture Notes in Computer Science, Vol. 11219. Ferrari, V.; Hebert, M.; Sminchisescu, C.; Weiss, Y. Eds. Springer Cham, 370-385, 2018.
[33]
Wang, W.; Zhao, S.; Shen, J.; Hoi, S. C.; Borji, A. Salient object detection with pyramid attention and salient edges. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 1448-1457, 2019.
Crossref
[34]
Su, J.; Li, J.; Zhang, Y.; Xia, C.; Tian, Y. Selectivity or invariance: Boundary-aware salient object detection. In: Proceedings of the IEEE International Conference on Computer Vision, 3799-3808, 2019.
Crossref
[35]
Zhao, T.; Wu, X. Pyramid feature attention network for saliency detection. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 3085-3094, 2019.
Crossref
[36]
Cong, R. M.; Lei, J. J.; Zhang, C. Q.; Huang, Q. M.; Cao, X. C.; Hou, C. P. Saliency detection for stereoscopic images based on depth confidence analysis and multiple cues fusion. IEEE Signal Processing Letters Vol. 23, No. 6, 819-823, 2016.
Crossref Google Scholar
[37]
Guo, J.; Ren, T.; Bei, J. Salient object detection for RGB-D image via saliency evolution. In: Proceedings of the IEEE International Conference on Multimedia and Expo, 1-6, 2016.
Crossref
[38]
Fan, D. P.; Lin, Z.; Zhang, Z.; Zhu, M. L.; Cheng, M. M. Rethinking RGB-D salient object detection: Models, data sets, and large-scale benchmarks. IEEE Transactions on Neural Networks and Learning Systems , 2020.
Crossref Google Scholar
[39]
Zhang, M.; Ren, W.; Piao, Y.; Rong, Z.; Lu, H. Select, supplement and focus for RGB-D saliency detection. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 3469-3478, 2020.
Crossref
[40]
Piao, Y.; Rong, Z.; Zhang, M.; Ren, W.; Lu, H. A2dele: Adaptive and attentive depth distiller for efficient RGB-D salient object detection. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 9057-9066, 2020.
Crossref
[41]
Liu, N.; Zhang, N.; Han, J. Learning selective self-mutual attention for RGB-D saliency detection. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2020.
Crossref
[42]
Li, G. Y.; Liu, Z.; Ling, H. B. ICNet: Information conversion network for RGB-D based salient object detection. IEEE Transactions on Image Processing Vol. 29, 4873-4884, 2020.
Crossref Google Scholar
[43]
Fu, K.; Fan, D.-P.; Ji, G.-P.; Zhao, Q. JL-DCF: Joint learning and densely-cooperative fusion framework for RGB-D salient object detection. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 3049-3059, 2020.
Crossref
[44]
Zhang, J.; Fan, D.-P.; Dai, Y.; Anwar, S.; Saleh, F. S.; Zhang, T.; Barnes, N. UC-Net: Uncertainty inspired RGB-D saliency detection via conditional variational autoencoders. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2020.
Crossref
[45]
Chen, H.; Li, Y. F. CNN-based RGB-D salient object detection: Learn, select and fuse. arXiv preprint arXiv:1909.09309, 2019.
Google Scholar
[46]
Lang, C. Y.; Nguyen, T. V.; Katti, H.; Yadati, K.; Kankanhalli, M.; Yan, S. C. Depth matters: influence of depth cues on visual saliency. In: Computer Vision - ECCV 2012. Lecture Notes in Computer Science, Vol. 7573. Fitzgibbon, A.; Lazebnik, S.; Perona, P.; Sato, Y.; Schmid, C. Eds. Springer Berlin Heidelberg, 101-115, 2012.
Crossref
[47]
Ciptadi, A.; Hermans, T.; Rehg, J. M. An in depth view of saliency. In: Proceedings of the 24th British Machine Vision Conference, 2013.
Crossref
[48]
Desingh, K.; Madhava Krishna, K.; Rajan, D.; Jawahar, C. V. Depth really matters: Improving visual salient region detection with depth. In: Proceedings of the British Machine Vision Conference, 98.1-98.11, 2013.
Crossref
[49]
Cheng, Y. P.; Fu, H. Z.; Wei, X. X.; Xiao, J. J.; Cao, X. C. Depth enhanced saliency detection method. In: Proceedings of the International Conference on Internet Multimedia Computing and Service, 23-27, 2014.
Crossref
[50]
Ren, J.; Gong, X.; Yu, L.; Zhou, W.; Yang, M. Y. Exploiting global priors for RGB-D saliency detection. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops, 25-32, 2015.
Crossref
[51]
Peng, H. W.; Li, B.; Xiong, W. H.; Hu, W. M.; Ji, R. R. RGBD salient object detection: A benchmark and algorithms. In: Computer Vision - ECCV 2014. Lecture Notes in Computer Science, Vol. 8691. Fleet, D.; Pajdla, T.; Schiele, B.; Tuytelaars, T. Eds. Springer Cham, 92-109, 2014.
[52]
Qu, L. Q.; He, S. F.; Zhang, J. W.; Tian, J. D.; Tang, Y. D.; Yang, Q. X. RGBD salient object detection via deep fusion. IEEE Transactions on Image Processing Vol. 26, No. 5, 2274-2285, 2017.
Crossref Google Scholar
[53]
Zhao, J.-X.; Cao, Y.; Fan, D.-P.; Cheng, M.-M.; Li, X.-Y.; Zhang, L. Contrast prior and fluid pyramid integration for RGBD salient object detection. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 3927-3936, 2019.
Crossref
[54]
Piao, Y.; Ji, W.; Li, J.; Zhang, M.; Lu, H. Depth-induced multi-scale recurrent attention network for saliency detection. In: Proceedings of the IEEE International Conference on Computer Vision, 7254-7263, 2019.
Crossref
[55]
Chen, H.; Li, Y. F.; Su, D. Multi-modal fusion network with multi-scale multi-path and cross-modal interactions for RGB-D salient object detection. Pattern Recognition Vol. 86, 376-385, 2019.
Crossref Google Scholar
[56]
Ju, R.; Ge, L.; Geng, W.; Ren, T.; Wu, G. Depth saliency based on anisotropic center-surround difierence. In: Proceedings of the IEEE International Conference on Image Processing, 1115-1119, 2014.
Crossref
[57]
Feng, D.; Barnes, N.; You, S.; McCarthy, C. Local background enclosure for RGB-D salient object detection. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2343-2350, 2016.
Crossref
[58]
Han, J. W.; Chen, H.; Liu, N.; Yan, C. G.; Li, X. L. CNNs-based RGB-D saliency detection via cross-view transfer and multiview fusion. IEEE Transactions on Cybernetics Vol. 48, No. 11, 3171-3183, 2018.
Crossref Google Scholar
[59]
Borji, A.; Cheng, M. M.; Jiang, H. Z.; Li, J. Salient object detection: A benchmark. IEEE Transactions on Image Processing Vol. 24, No. 12, 5706-5722, 2015.
Crossref Google Scholar
[60]
Cong, R.; Lei, J.; Fu, H.; Cheng, M.-M.; Lin, W.; Huang, Q. Review of visual saliency detection with comprehensive information. IEEE Transactions on Circuits and Systems for Video Technology Vol. 29, No. 10, 2941-2959, 2018.
Crossref Google Scholar
[61]
Zhang, D.; Fu, H.; Han, J.; Borji, A.; Li, X. A review of co-saliency detection algorithms: Fundamentals, applications, and challenges. ACM Transactions on Intelligent Systems and Technology Vol. 9, No. 4, 1-31, 2018.
Crossref Google Scholar
[62]
Han, J. W.; Zhang, D. W.; Cheng, G.; Liu, N.; Xu, D. Advanced deep-learning techniques for salient and category-specific object detection: A survey. IEEE Signal Processing Magazine Vol. 35, No. 1, 84-100, 2018.
Crossref Google Scholar
[63]
Nguyen, T. V.; Zhao, Q.; Yan, S. C. Attentive systems: A survey. International Journal of Computer Vision Vol. 126, No. 1, 86-110, 2018.
Crossref Google Scholar
[64]
Borji, A.; Cheng, M. M.; Hou, Q. B.; Jiang, H. Z.; Li, J. Salient object detection: A survey. Computational Visual Media Vol. 5, No. 2, 117-150, 2019.
Crossref Google Scholar
[65]
Zhao, Z. Q.; Zheng, P.; Xu, S. T.; Wu, X. D. Object detection with deep learning: A review. IEEE Transactions on Neural Networks and Learning Systems Vol. 30, No. 11, 3212-3232, 2019.
Crossref Google Scholar
[66]
Wang, W. G.; Lai, Q. X.; Fu, H. Z.; Shen, J. B.; Ling, H. B.; Yang, R. G. Salient object detection in the deep learning era: An in-depth survey. arXiv preprint arXiv:1904.09146, 2019.
Google Scholar
[67]
Zhang, H.; Lei, J.; Fan, X.; Wu, M.; Zhang, P.; Bu, S. Depth combined saliency detection based on region contrast model. In: Proceedings of International Conference on Computer Science & Education, 763-766, 2012.
Crossref
[68]
Lei, J. J.; Zhang, H. L.; You, L.; Hou, C. P.; Wang, L. H. Evaluation and modeling of depth feature incorporated visual attention for salient object segmentation. Neurocomputing Vol. 120, 24-33, 2013.
Crossref Google Scholar
[69]
Fan, X.; Liu, Z.; Sun, G. Salient region detection for stereoscopic images. In: Proceedings of the International Conference on Digital Signal Processing, 454-458, 2014.
Crossref
[70]
Guo, J. F.; Ren, T. W.; Bei, J.; Zhu, Y. J. Salient object detection in RGB-D image based on saliency fusion and propagation. In: Proceedings of the 7th International Conference on Internet Multimedia Computing and Service, Article No. 59, 2015.
Crossref
[71]
Tang, Y. L.; Tong, R. F.; Tang, M.; Zhang, Y. Depth incorporating with color improves salient object detection. The Visual Computer Vol. 32, No. 1, 111-121, 2016.
Crossref Google Scholar
[72]
Jiang, L.; Koch, A.; Zell, A. Salient regions detection for indoor robots using RGB-D data. In: Proceedings of the IEEE International Conference on Robotics and Automation, 1323-1328, 2015.
Crossref
[73]
Xue, H.; Gu, Y.; Li, Y.; Yang, J. RGB-D saliency detection via mutual guided manifold ranking. In: Proceedings of IEEE International Conference on Image Processing, 666-670, 2015.
Crossref
[74]
Zhu, L.; Cao, Z.; Fang, Z.; Xiao, Y.; Wu, J.; Deng, H.; Liu, J. Selective features for RGB-D saliency. In: Proceedings of Chinese Automation Congress, 512-517, 2015.
Crossref
[75]
Du, H.; Liu, Z.; Song, H. K.; Mei, L.; Xu, Z. Improving RGBD saliency detection using progressive region classification and saliency fusion. IEEE Access Vol. 4, 8987-8994, 2016.
Crossref Google Scholar
[76]
Wang, S.-T.; Zhou, Z.; Qu, H.-B.; Li, B. RGBD saliency detection under bayesian framework. In: Proceedings of the 23rd International Conference on Pattern Recognition, 1881-1886, 2016.
[77]
Sheng, H.; Liu, X.; Zhang, S. Saliency analysis based on depth contrast increased. In: Proceedings of IEEE International Conference on Acoustics, Speech and Signal Processing, 1347-1351, 2016.
Crossref
[78]
Song, H.; Liu, Z.; Du, H.; Sun, G. Depth-aware saliency detection using discriminative saliency fusion. In: Proceedings of IEEE International Conference on Acoustics, Speech and Signal Processing, 1626-1630, 2016.
Crossref
[79]
Wang, S. T.; Zhou, Z.; Qu, H. B.; Li, B. Visual saliency detection for RGB-D images with generative model. In: Computer Vision - ACCV 2016. Lecture Notes in Computer Science, Vol. 10115. Lai, S. H.; Lepetit, V.; Nishino, K.; Sato, Y. Eds. Springer Cham, 20-35, 2017.
[80]
Feng, D.; Barnes, N.; You, S. HOSO: Histogram of surface orientation for RGB-D salient object detection. In Proceedings of the International Conference on Digital Image Computing: Techniques and Applications, 1-8, 2017.
Crossref
[81]
Chen, H.; Li, Y.-F.; Su, D. M3Net: Multi-scale multi-path multi-modal fusion network and example application to RGB-D salient object detection. In: Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems, 4911-4916, 2017.
Crossref
[82]
Chen, H.; Li, Y. F.; Su, D. RGB-D saliency detection by multi-stream late fusion network. In: Computer Vision Systems. Lecture Notes in Computer Science, Vol. 10528. Liu, M.; Chen, H.; Vincze, M. Eds. Springer Cham, 459-468, 2017.
Crossref
[83]
Shigematsu, R.; Feng, D.; You, S.; Barnes, N. Learning RGB-D salient object detection using background enclosure, depth contrast, and top-down features. In: Proceedings of the IEEE International Conference on Computer Vision Workshops, 2749-2757, 2017.
Crossref
[84]
Zhu, C.; Li, G.; Wang, W.; Wang, R. An innovative salient object detection using center-dark channel prior. In: Proceedings of the IEEE International Conference on Computer Vision Workshops, 1509-1515, 2017.
Crossref
[85]
Zhu, C.; Li, G. A three-pathway psychobiological framework of salient object detection using stereoscopic technology. In: Proceedings of the IEEE International Conference on Computer Vision Workshops, 3008-3014, 2017.
[86]
Wang, A. Z.; Wang, M. H. RGB-D salient object detection via minimum barrier distance transform and saliency fusion. IEEE Signal Processing Letters Vol. 24, No. 5, 663-667, 2017.
Crossref Google Scholar
[87]
Song, H. K.; Liu, Z.; Du, H.; Sun, G. L.; Le Meur, O., Ren, T. W. Depth-aware salient object detection and segmentation via multiscale discriminative saliency fusion and bootstrap learning. IEEE Transactions on Image Processing Vol. 26, No. 9, 4204-4216, 2017.
Crossref Google Scholar
[88]
Cong, R. M.; Lei, J. J.; Fu, H. Z.; Lin, W. S.; Huang, Q. M.; Cao, X. C.; Hou, C. P. An iterative co-saliency framework for RGBD images. IEEE Transactions on Cybernetics Vol. 49, No. 1, 233-246, 2019.
Crossref Google Scholar
[89]
Imamoglu, N.; Shimoda, W.; Zhang, C.; Fang, Y. M.; Kanezaki, A.; Yanai, K.; Nishida, Y. An integration of bottom-up and top-down salient cues on RGB-D data: Saliency from objectness versus non-objectness. Signal, Image and Video Processing Vol. 12, No. 2, 307-314, 2018.
Crossref Google Scholar
[90]
Cong, R. M.; Lei, J. J.; Fu, H. Z.; Huang, Q. M.; Cao, X. C.; Ling, N. HSCS: Hierarchical sparsity based Co-saliency detection for RGBD images. IEEE Transactions on Multimedia Vol. 21, No. 7, 1660-1671, 2019.
Crossref Google Scholar
[91]
Cong, R. M.; Lei, J. J.; Fu, H. Z.; Huang, Q. M.; Cao, X. C.; Hou, C. P. Co-saliency detection for RGBD images based on multi-constraint feature matching and cross label propagation. IEEE Transactions on Image Processing Vol. 27, No. 2, 568-579, 2018.
Crossref Google Scholar
[92]
Chen, H.; Li, Y. Progressively complementarity-aware fusion network for RGB-D salient object detection. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 3051-3060, 2018.
Crossref
[93]
Huang, P.; Shen, C.-H.; Hsiao, H.-F. RGBD salient object detection using spatially coherent deep learning framework. In: Proceedings of the IEEE International Conference on Digital Signal Processing, 1-5, 2018.
Crossref
[94]
Chen, H.; Li, Y.-F.; Su, D. Attention-aware crossmodal cross-level fusion network for RGB-D salient object detection. In: Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems, 6821-6826, 2018.
Crossref
[95]
Liang, F. F.; Duan, L. J.; Ma, W.; Qiao, Y. H.; Cai, Z.; Qing, L. Y. Stereoscopic saliency model using contrast and depth-guided-background prior. Neurocomputing Vol. 275, 2227-2238, 2018.
Crossref Google Scholar
[96]
Liu, Z. Y.; Shi, S.; Duan, Q. T.; Zhang, W.; Zhao, P. Salient object detection for RGB-D image by single stream recurrent convolution neural network. Neurocomputing Vol. 363, 46-57, 2019.
Crossref Google Scholar
[97]
Huang, R.; Xing, Y.; Wang, Z. Z. RGB-D salient object detection by a CNN with multiple layers fusion. IEEE Signal Processing Letters Vol. 26, No. 4, 552-556, 2019.
Crossref Google Scholar
[98]
Liu, D.; Hu, Y.; Zhang, K.; Chen, Z. Two-stream refinement network for RGB-D saliency detection. In: Proceedings of IEEE International Conference on Image Processing, 3925-3929, 2019.
Crossref
[99]
Du, H.; Liu, Z.; Shi, R. Salient object segmentation based on depth-aware image layering. Multimedia Tools and Applications Vol. 78, No. 9, 12125-12138, 2019.
Crossref Google Scholar
[100]
Zhou, W. J.; Lv, Y., Lei, J. S.; Yu, L. Global and local-contrast guides content-aware fusion for RGB-D saliency prediction. IEEE Transactions on Systems, Man, and Cybernetics: Systems , 2019.
Crossref Google Scholar
[101]
Ma, C. Y.; Hang, H. M. Learning-based saliency model with depth information. Journal of Vision Vol. 15, No. 6, 19, 2015.
Crossref Google Scholar
[102]
Zhu, C.; Cai, X.; Huang, K.; Li, T. H.; Li, G. PDNet: Prior-model guided depth-enhanced network for salient object detection. In: Proceedings of the IEEE International Conference on Multimedia and Expo, 199-204, 2019.
Crossref
[103]
Chen, H.; Li, Y. F. Three-stream attention-aware network for RGB-D salient object detection. IEEE Transactions on Image Processing Vol. 28, No. 6, 2825-2835, 2019.
Crossref Google Scholar
[104]
Chen, H.; Li, Y. F.; Su, D. Discriminative cross-modal transfer learning and densely cross-level feedback fusion for RGB-D salient object detection. IEEE Transactions on Cybernetics Vol. 50, No. 11, 4808-4820, 2020.
Crossref Google Scholar
[105]
Cong, R. M.; Lei, J. J.; Fu, H. Z.; Hou, J. H.; Huang, Q. M.; Kwong, S. Going from RGB to RGBD saliency: A depth-guided transformation model. IEEE Transactions on Cybernetics Vol. 50, No. 8, 3627-3639, 2020.
Crossref Google Scholar
[106]
Wang, N. N.; Gong, X. J. Adaptive fusion for RGB-D salient object detection. IEEE Access Vol. 7, 55277-55284, 2019.
Crossref Google Scholar
[107]
Jin, Z. G.; Li, J. K.; Li, D. Co-saliency detection for RGBD images based on effective propagation mechanism. IEEE Access Vol. 7, 141311-141318,2019.
Crossref Google Scholar
[108]
Ding, Y.; Liu, Z.; Huang, M. K.; Shi, R.; Wang, X. Y. Depth-aware saliency detection using convolutional neural networks. Journal of Visual Communication and Image Representation Vol. 61, 1-9, 2019.
Crossref Google Scholar
[109]
Chen, Z.; Huang, Q. Depth potentiality-aware gated attention network for RGB-D salient object detection. arXiv preprint arXiv:2003.08608, 2020.
Google Scholar
[110]
Wang, Y.; Li, Y. K.; Elder, J. H.; Lu, H. C.; Wu, R. M.; Zhang, L. Synergistic saliency and depth prediction for RGB-D saliency detection. arXiv preprint arXiv:2007.01711, 2020.
Google Scholar
[111]
Zhou, X. F.; Li, G. Y.; Gong, C.; Liu, Z.; Zhang, J. Y. Attention-guided RGBD saliency detection using appearance information. Image and Vision Computing Vol. 95, 103888, 2020.
Crossref Google Scholar
[112]
Liu, Z. Y.; Zhang, W.; Zhao, P. A cross-modal adaptive gated fusion generative adversarial network for RGB-D salient object detection. Neurocomputing Vol. 387, 210-220, 2020.
Crossref Google Scholar
[113]
Liang, F. F.; Duan, L. J.; Ma, W.; Qiao, Y. H.; Cai, Z.; Miao, J.; Ye, Q. CoCNN: RGB-D deep fusion for stereoscopic salient object detection. Pattern Recognition Vol. 104, 107329, 2020.
Crossref Google Scholar
[114]
Jiang, B.; Zhou, Z. T.; Wang, X.; Tang, J.; Luo, B. cmSalGAN: RGB-D salient object detection with cross-view generative adversarial networks. IEEE Transactions on Multimedia , 2020.
Crossref Google Scholar
[115]
Xiao, F.; Li, B.; Peng, Y. M.; Cao, C. H.; Hu, K.; Gao, X. P. Multi-modal weights sharing and hierarchical feature fusion for RGBD salient object detection. IEEE Access Vol. 8, 26602-26611, 2020.
Crossref Google Scholar
[116]
Zhang, Z.; Lin, Z.; Xu, J.; Jin, W. D.; Lu, S. P.; Fan, D. P. Bilateral attention network for RGB-D salient object detection. arXiv preprint arXiv:2004.14582, 2020.
Google Scholar
[117]
Li, C. Y.; Cong, R. M.; Kwong, S.; Hou, J. H.; Fu, H. Z.; Zhu, G. P.; Zhang, D.; Huang, Q. ASIF-Net: Attention steered interweave fusion network for RGB-D salient object detection. IEEE Transactions on Cybernetics , 2020.
Crossref Google Scholar
[118]
Huang, R.; Xing, Y.; Zou, Y. B. Triple-complementary network for RGB-D salient object detection. IEEE Signal Processing Letters Vol. 27, 775-779, 2020.
Crossref Google Scholar
[119]
Chen, C.; Wei, J. P.; Peng, C.; Zhang, W. Z.; Qin, H. Improved saliency detection in RGB-D images using two-phase depth estimation and selective deep fusion. IEEE Transactions on Image Processing Vol. 29, 4296-4307, 2020.
Crossref Google Scholar
[120]
Zhou, W. J.; Chen, Y. Z.; Liu, C.; Yu, L. GFNet: Gate fusion network with Res2Net for detecting salient objects in RGB-D images. IEEE Signal Processing Letters Vol. 27, 800-804, 2020.
Crossref Google Scholar
[121]
Liu, Z. Y.; Tang, J. T.; Xiang, Q.; Zhao, P. Salient object detection for RGB-D images by generative adversarial network. Multimedia Tools and Applications Vol. 79, Nos. 35-36, 25403-25425, 2020.
Crossref Google Scholar
[122]
Li, G. Y.; Liu, Z.; Ye, L. W.; Wang, Y.; Ling, H. B. Cross-modal weighting network for RGB-D salient object detection. In: Computer Vision - ECCV 2020. Lecture Notes in Computer Science, Vol. 12362. Vedaldi, A.; Bischof, H.; Brox, T.; Frahm, J. M. Eds. Springer Cham, 665-681, 2020.
[123]
Pang, Y. W.; Zhang, L. H.; Zhao, X. Q.; Lu, H. C. Hierarchical dynamic filtering network for RGB-D salient object detection. In: Computer Vision - ECCV 2020. Lecture Notes in Computer Science, Vol. 12370. Vedaldi, A.; Bischof, H.; Brox, T.; Frahm, J. M. Eds. Springer Cham, 235-252, 2020.
[124]
Luo, A.; Li, X.; Yang, F.; Jiao, Z. C.; Cheng, H.; Lyu, S. W. Cascade graph neural networks for RGB-D salient object detection. In: Computer Vision - ECCV 2020. Lecture Notes in Computer Science, Vol. 12357. Vedaldi, A.; Bischof, H.; Brox, T.; Frahm, J. M. Eds. Springer Cham, 346-364, 2020.
[125]
Li, C. Y.; Cong, R. M.; Piao, Y. R.; Xu, Q. Q.; Loy, C. C. RGB-D salient object detection with cross-modality modulation and selection. In: Computer Vision - ECCV 2020. Lecture Notes in Computer Science, Vol. 12353. Vedaldi, A.; Bischof, H.; Brox, T.; Frahm, J. M. Eds. Springer Cham, 225-241, 2020.
[126]
Zhao, X. Q.; Zhang, L. H.; Pang, Y. W.; Lu, H. C.; Zhang, L. A single stream network for robust and real-time RGB-D salient object detection. In: Computer Vision - ECCV 2020. Lecture Notes in Computer Science, Vol. 12367. Vedaldi, A.; Bischof, H.; Brox, T.; Frahm, J. M. Eds. Springer Cham, 646-662, 2020.
[127]
Ji, W.; Li, J. J.; Zhang, M.; Piao, Y. R.; Lu, H. C. Accurate RGB-D salient object detection via collaborative learning. In: Computer Vision - ECCV 2020. Lecture Notes in Computer Science, Vol. 12363. Vedaldi, A.; Bischof, H.; Brox, T.; Frahm, J. M. Eds. Springer Cham, 52-69, 2020.
[128]
Fan, D. P.; Zhai, Y. J.; Borji, A.; Yang, J. F.; Shao, L. BBS-Net: RGB-D salient object detection with a bifurcated backbone strategy network. In: Computer Vision - ECCV 2020. Lecture Notes in Computer Science, Vol. 12357. Vedaldi, A.; Bischof, H.; Brox, T.; Frahm, J. M. Eds. Springer Cham, 275-292,2020.
[129]
Zhang, M.; Fei, S. X.; Liu, J.; Xu, S.; Piao, Y. R.; Lu, H. C. Asymmetric two-stream architecture for accurate RGB-D saliency detection. In: Computer Vision - ECCV 2020. Lecture Notes in Computer Science, Vol. 12373. Vedaldi, A.; Bischof, H.; Brox, T.; Frahm, J. M. Eds. Springer Cham, 374-390, 2020.
[130]
Chen, S. H.; Fu, Y. Progressively guided alternate refinement network for RGB-D salient object detection. In: Computer Vision - ECCV 2020. Lecture Notes in Computer Science, Vol. 12353. Vedaldi, A.; Bischof, H.; Brox, T.; Frahm, J. M. Eds. Springer Cham, 520-538, 2020.
[131]
Huang, Z.; Chen, H. X.; Zhou, T.; Yang, Y. Z.; Wang, C. Y. Multi-level cross-modal interaction network for RGB-D salient object detection. arXiv preprint arXiv:2007.14352, 2020.
Google Scholar
[132]
Wang, X. H.; Li, S.; Chen, C., Fang, Y. M.; Hao, A. M.; Qin, H. Data-level recombination and lightweight fusion scheme for RGB-D salient object detection. IEEE Transactions on Image Processing Vol. 30, 458-471, 2021.
Crossref Google Scholar
[133]
Wang, X.; Li, S.; Chen, C.; Hao, A.; Qin, H. Knowing depth quality in advance: A depth quality assessment method for RGB-D salient object detection. arXiv preprint arXiv:2008.04157, 2020.
Google Scholar
[134]
Chen, C.; Wei, J.; Peng, C.; Qin, H. Depth quality aware salient object detection. arXiv preprint arXiv:2008.04159, 2020.
Google Scholar
[135]
Zhao, J. W.; Zhao, Y. F.; Li, J.; Chen, X. W. Is depth really necessary for salient object detection. arXiv preprint arXiv:2006.00269, 2020.
Google Scholar
[136]
Chen, H.; Deng, Y. J.; Li, Y. F.; Hung, T. Y.; Lin, G. S. RGBD salient object detection via disentangled cross-modal fusion. IEEE Transactions on Image Processing Vol. 29, 8407-8416, 2020.
Crossref Google Scholar
[137]
Piao, Y. R.; Li, X.; Zhang, M.; Yu, J. Y.; Lu, H. C. Saliency detection via depth-induced cellular automata on light field. IEEE Transactions on Image Processing Vol. 29, 1879-1889, 2020.
Crossref Google Scholar
[138]
Zhang, M.; Zhang, Y.; Piao, Y. R.; Hu, B. Q.; Lu, H. C. Feature reintegration over differential treatment: A top-down and adaptive fusion network for RGB-D salient object detection. In: Proceedings of the 28th ACM International Conference on Multimedia, 4107-4115, 2020.
Crossref
[139]
Niu, Y.; Geng, Y.; Li, X.; Liu, F. Leveraging stereopsis for saliency analysis. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 454-461, 2012.
[140]
Li, N.; Ye, J.; Ji, Y.; Ling, H.; Yu, J. Saliency detection on light field. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2806-2813, 2014.
Crossref
[141]
Zhang, M.; Ji, W.; Piao, Y. R.; Li, J. J.; Zhang, Y.; Xu, S.; Lu, H. LFNet: Light field fusion network for salient object detection. IEEE Transactions on Image Processing Vol. 29, 6276-6287, 2020.
Crossref Google Scholar
[142]
Li, N.; Sun, B.; Yu, J. A weighted sparse coding framework for saliency detection. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 5216-5223, 2015.
Crossref
[143]
Zhang, J.; Wang, M.; Gao, J.; Wang, Y.; Zhang, X.; Wu, X. Saliency detection with a deeper investigation of light field. In: Proceedings of the International Joint Conference on Artificial Intelligence, 2212-2218, 2015.
[144]
Sheng, H.; Zhang, S.; Liu, X.; Xiong, Z. Relative location for light field saliency detection. In: Proceedings of the IEEE Conference on Acoustics, Speech and Signal Processing, 1631-1635, 2016.
Crossref
[145]
Zhang, J.; Wang, M.; Lin, L.; Yang, X.; Gao, J.; Rui, Y. Saliency detection on light field. ACM Transactions on Multimedia Computing, Communications, and Applications Vol. 13, No. 3, 1-22, 2017.
Crossref Google Scholar
[146]
Wang, A. Z.; Wang, M. H.; Li, X. Y.; Mi, Z. T.; Zhou, H. A two-stage Bayesian integration framework for salient object detection on light field. Neural Processing Letters Vol. 46, No. 3, 1083-1094, 2017.
Crossref Google Scholar
[147]
Li, N. Y.; Ye, J. W.; Ji, Y.; Ling, H. B.; Yu, J. Y. Saliency detection on light field. IEEE Transactions on Pattern Analysis and Machine Intelligence Vol. 39, No. 8, 1605-1616, 2017.
Crossref Google Scholar
[148]
Li, C.; Zhan, B.; Zhang, S.; Sheng, H. Saliency detection with relative location measure in light field image. In: Proceedings of the International Conference on Image, Vision and Computing, 8-12, 2017.
[149]
Wang, S.; Liao, W.; Surman, P.; Tu, Z.; Zheng, Y.; Yuan, J. Salience guided depth calibration for perceptually optimized compressive light field 3D display. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2031-2040, 2018.
Crossref
[150]
Piao, Y. R.; Li, X.; Zhang, M. Depth-induced cellular automata for light field saliency. In: Proceedings of the Frontiers in Optics/Laser Science, OSA Technical Digest, FTh3E.3, 2018.
Crossref
[151]
Wang, T.; Piao, Y.; Li, X.; Zhang, L.; Lu, H. Deep learning for light field saliency detection. In: Proceedings of the IEEE International Conference on Computer Vision, 8838-8848, 2019.
Crossref
[152]
Piao, Y. R.; Rong, Z. K.; Zhang, M.; Li, X.; Lu, H. C. Deep light-field-driven saliency detection from a single view. In: Proceedings of the 28th International Joint Conference on Artificial Intelligence, 904-911, 2019.
Crossref
[153]
Zhang, M.; Li, J.; WEI, J.; Piao, Y.; Lu, H. Memory-oriented decoder for light field salient object detection. In: Proceedings of the International Conference on Neural Information Processing Systems, 896-906, 2019.
[154]
Piao, Y. R.; Rong, Z. K.; Zhang, M.; Lu, H. C. Exploit and replace: An asymmetrical two-stream architecture for versatile light field saliency detection. In: Proceedings of the AAAI Conference on Artificial Intelligence, Vol. 34, No. 7, 11865-11873, 2020.
Crossref
[155]
Wang, X.; Dong, Y. Y.; Zhang, Q.; Wang, Q. Regionbased depth feature descriptor for saliency detection on light field. Multimedia Tools and Applications , 2020.
Crossref Google Scholar
[156]
Zhang, J.; Liu, Y. M.; Zhang, S. P.; Poppe, R., Wang, M. Light field saliency detection with deep convolutional networks. IEEE Transactions on Image Processing Vol. 29, 4421-4434, 2020.
Crossref Google Scholar
[157]
Achanta, R.; Hemami, S.; Estrada, F.; Susstrunk, S. Frequency-tuned salient region detection. In: Proceedings of the IEEE conference on Computer Vision and Pattern Recognition, 1597-1604, 2009.
Crossref
[158]
Krahenbuhl, P. Saliency filters: Contrast based filtering for salient region detection. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 733-740, 2012.
[159]
Fan, D.-P.; Cheng, M.-M.; Liu, Y.; Li, T.; Borji. A. Structure-measure: A new way to evaluate foreground maps. In: Proceedings of the IEEE International Conference on Computer Vision, 4548-4557, 2017.
Crossref
[160]
Fan, D. P.; Gong, C.; Cao, Y.; Ren, B.; Cheng, M. M.; Borji, A. Enhanced-alignment measure for binary foreground map evaluation. In: Proceedings of the 27th International Joint Conference on Artificial Intelligence, 698-704, 2018.
Crossref
[161]
Qin, Y.; Lu, H.; Xu, Y.; Wang, H. Saliency detection via cellular automata. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 110-119, 2015.
Crossref
[162]
Zhou, L.; Yang, Z. H.; Yuan, Q.; Zhou, Z. T.; Hu, D. W. Salient region detection via integrating diffusion-based compactness and local contrast. IEEE Transactions on Image Processing Vol. 24, No. 11, 3308-3320, 2015.
Crossref Google Scholar
[163]
Huang, X. M.; Zhang, Y. J. 300-FPS salient object detection via minimum directional contrast. IEEE Transactions on Image Processing Vol. 26, No. 9, 4243-4254, 2017.
Crossref Google Scholar
[164]
Huang, F.; Qi, J. Q.; Lu, H. C.; Zhang, L. H.; Ruan, X. Salient object detection via multiple instance learning. IEEE Transactions on Image Processing Vol. 26, No. 4, 1911-1922, 2017.
Crossref Google Scholar
[165]
Huang, X. M.; Zhang, Y. J. Water flow driven salient object detection at 180 fps. Pattern Recognition Vol. 76, 95-107, 2018.
Crossref Google Scholar
[166]
Xu, C.; Tao, D. C.; Xu, C. Multi-view learning with incomplete views. IEEE Transactions on Image Processing Vol. 24, No. 12, 5812-5825, 2015.
Crossref Google Scholar
[167]
Zhou, T.; Thung, K. H.; Zhu, X. F.; Shen, D. G. Effective feature learning and fusion of multimodality data using stage-wise deep neural network for dementia diagnosis. Human Brain Mapping Vol. 40, No. 3, 1001-1016, 2019.
Crossref Google Scholar
[168]
Zhou, T.; Liu, M. X.; Thung, K. H.; Shen, D. G. Latent representation learning for Alzheimer’s disease diagnosis with incomplete multi-modality neuroimaging and genetic data. IEEE Transactions on Medical Imaging Vol. 38, No. 10, 2411-2422, 2019.
Crossref Google Scholar
[169]
Zhou, T.; Thung, K. H.; Liu, M. X.; Shi, F.; Zhang, C. Q.; Shen, D. G. Multi-modal latent space inducing ensemble SVM classifier for early dementia diagnosis with neuroimaging data. Medical Image Analysis Vol. 60, 101630, 2020.
Crossref Google Scholar
[170]
Zhou, T.; Fu, H. Z.; Chen, G.; Shen, J. B.; Shao, L. Hi-net: Hybrid-fusion network for multi-modal MR image synthesis. IEEE Transactions on Medical Imaging Vol. 39, No. 9, 2772-2781, 2020.
Crossref Google Scholar
[171]
Godard, C.; Aodha, O. M.; Brostow, G. J. Unsupervised monocular depth estimation with left-right consistency. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 6602-6611, 2017.
Crossref
[172]
Liu, F.; Shen, C.; Lin, G. Deep convolutional neural fields for depth estimation from a single image. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 5162-5170, 2015.
Crossref
[173]
Wang, L.; Zhang, J.; Wang, O.; Lin, Z.; Lu, H. SDC-depth: Semantic divide-and-conquer network for monocular depth estimation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 541-550, 2020.
Crossref
[174]
Jin, L.; Xu, Y.; Zheng, J.; Zhang, J.; Tang, R.; Xu, S.; Yu, J.; Gao, S. Geometric structure based and regularized depth estimation from 360 indoor imagery. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 889-898, 2020.
Crossref
[175]
Mirza, M.; Osindero, S. Conditional generative adversarial nets. arXiv preprint arXiv:1411.1784, 2014.
Google Scholar
[176]
Zhu, D. D.; Dai, L.; Luo, Y.; Zhang, G. K.; Lu, J. W. Multi-scale adversarial feature learning for saliency detection. Symmetry Vol. 10, No. 10, 457, 2018.
Crossref Google Scholar
[177]
Pan, J. T.; Ferrer, C. C.; McGuinness, K.; O’Connor, N. E.; Torres, J.; Sayrol, E.; Giro-i-Nieto, X. SalGAN: Visual saliency prediction with generative adversarial networks. arXiv preprint arXiv:1701.01081, 2017.
Google Scholar
[178]
Vaswani, A.; Shazeer, N.; Parmar, N.; Uszkoreit, J.; Jones, L.; Gomez, A. N.; Kaiser, L.; Polosukhin, I. Attention is all you need. In: Proceedings of the Conference on Neural Information Processing Systems, 5998-6008, 2017.
[179]
Wang, F.; Jiang, M.; Qian, C.; Yang, S.; Li, C.; Zhang, H.; Wang, X.; Tang, X. Residual attention network for image classification. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 3156-3164, 2017.
Crossref
[180]
Fang, H. S.; Cao, J. K.; Tai, Y. W.; Lu, C. W. Pairwise body-part attention for recognizing human-object interactions. In: Computer Vision - ECCV 2018. Lecture Notes in Computer Science, Vol. 11214. Ferrari, V.; Hebert, M.; Sminchisescu, C.; Weiss, Y. Eds. Springer Cham, 52-68, 2018.
[181]
Wang, W. G.; Shen, J. B. Deep visual attention prediction. IEEE Transactions on Image Processing Vol. 27, No. 5, 2368-2378, 2018.
Crossref Google Scholar
[182]
Lu, J.; Yang, J.; Batra, D.; Parikh, D. Hierarchical question-image co-attention for visual question answering. In: Proceedings of the International Conference on Neural Information Processing Systems, 289-297, 2016.
[183]
Yu, Z.; Yu, J.; Cui, Y.; Tao, D.; Tian, Q. Deep modular co-attention networks for visual question answering. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 6281-6290, 2019.
Crossref
[184]
Lu, X.; Wang, W.; Ma, C.; Shen, J.; Shao, L.; Porikli, F. See more, know more: Unsupervised video object segmentation with co-attention siamese networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 3623-3632, 2019.
Crossref
[185]
Zeng, Y.; Zhuge, Y.; Lu, H.; Zhang, L.; Qian, M.; Yu, Y. Multi-source weak supervision for saliency detection. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 6074-6083, 2019.
Crossref
[186]
Zhang, D. W.; Meng, D. Y.; Zhao, L.; Han, J. W. Bridging saliency detection to weakly supervised object detection based on self-paced curriculum learning. arXiv preprint arXiv:1703.01290, 2017.
Google Scholar
[187]
Qian, M. Y.; Qi, J. Q.; Zhang, L. H.; Feng, M. Y.; Lu, H. C. Language-aware weak supervision for salient object detection. Pattern Recognition Vol. 96, 106955, 2019.
Crossref Google Scholar
[188]
Yan, P.; Li, G.; Xie, Y.; Li, Z.; Wang, C.; Chen, T.; Lin, L. Semi-supervised video salient object detection using pseudo-labels. In: Proceedings of the IEEE International Conference on Computer Vision, 7284-7293, 2019.
Crossref
[189]
Zhou, Y.; Huo, S. W.; Xiang, W.; Hou, C. P.; Kung, S. Y. Semi-supervised salient object detection using a linear feedback control system model. IEEE Transactions on Cybernetics Vol. 49, No. 4, 1173-1185, 2019.
Crossref Google Scholar
[190]
Zhang, D.; Han, J.; Zhang, Y. Supervision by fusion: Towards unsupervised learning of deep salient object detector. In: Proceedings of the IEEE International Conference on Computer Vision, 4048-4056, 2017.
Crossref
[191]
Chen, T.; Liu, S.; Chang, S.; Cheng, Y.; Amini, L.; Wang, Z. Adversarial robustness: From self-supervised pre-training to fine-tuning. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 699-708, 2020.
Crossref
[192]
Dai, A.; Diller, C.; Niefiner, M. SG-NN: Sparse generative neural networks for self-supervised scene completion of RGB-D scans. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 849-858, 2020.
Crossref
[193]
Lai, K.; Bo, L.; Ren, X.; Fox, D. A largescale hierarchical multi-view RGB-D object dataset. In: Proceedings of the IEEE International Conference on Robotics and Automation, 1817-1824, 2011.
Crossref
[194]
Zhang, J.; Li, W. Q.; Wang, P. C.; Ogunbona, P., Liu, S.; Tang, C. A large scale RGB-D dataset for action recognition. In: Understanding Human Activities Through 3D Sensors. Lecture Notes in Computer Science, Vol. 10188. Wannous, H.; Pala, P.; Daoudi, M.; Flórez-Revuelta, F. Eds. Springer Cham, 101-114, 2018.
[195]
He, Y. H.; Lin, J.; Liu, Z. J.; Wang, H. R.; Li, L. J.; Han, S. AMC: AutoML for model compression and acceleration on mobile devices. In: Computer Vision - ECCV 2018. Lecture Notes in Computer Science, Vol. 11211. Ferrari, V.; Hebert, M.; Sminchisescu, C.; Weiss, Y. Eds. Springer Cham, 815-832, 2018.
[196]
Cheng, Y.; Wang, D.; Zhou, P.; Zhang, T. A survey of model compression and acceleration for deep neural networks. arXiv preprint arXiv:1710.09282, 2017.
Google Scholar
[197]
Ma, Y.; Sun, D.; Meng, Q.; Ding, Z.; Li, C. Learning multiscale deep features and SVM regressors for adaptive RGB-T saliency detection. In: Proceedings of the 10th International Symposium on Computational Intelligence and Design, 389-392, 2017.
Crossref
[198]
Wang, G. Z.; Li, C. L.; Ma, Y. P.; Zheng, A. H.; Tang, J.; Luo, B. RGB-T saliency detection benchmark: Dataset, baselines, analysis and a novel approach. In: Image and Graphics Technologies and Applications. Communications in Computer and Information Science, Vol. 875. Wang, Y.; Jiang, Z.; Peng, Y. Eds. Springer Singapore, 359-369, 2018.
Crossref
[199]
Wang, G. Z.; Li, C. L.; Ma, Y. P.; Zheng, A. H.; Tang, J.; Luo, B. RGB-T saliency detection benchmark: Dataset, baselines, analysis and a novel approach. In: Image and Graphics Technologies and Applications. Communications in Computer and Information Science, Vol. 875. Wang, Y.; Jiang, Z.; Peng, Y. Eds. Springer Singapore, 359-369, 2018.
Crossref
[200]
Sun, D. D.; Li, S.; Ding, Z. L.; Luo, B. RGB-T saliency detection via robust graph learning and collaborative manifold ranking. In: Bio-inspired Computing: Theories and Applications. Communications in Computer and Information Science, Vol. 1160. Pan, L.; Liang, J.; Qu, B. Eds. Springer Singapore, 670-684, 2020.
Crossref
[201]
Tu, Z.; Xia, T.; Li, C.; Lu, Y.; Tang, J. M3S-NIR: Multi-modal multi-scale noise-insensitive ranking for RGB-T saliency detection. In: Proceedings of the IEEE Conference on Multimedia Information Processing and Retrieval, 141-146, 2019.
Crossref
[202]
Tu, Z. Z.; Li, Z.; Li, C. L.; Lang, Y.; Tang, J. Multi-interactive encoder-decoder network for RGBT salient object detection. arXiv preprint arXiv:2005.02315, 2020.
Google Scholar
[203]
Tu, Z. Z.; Xia, T.; Li, C. L.; Wang, X. X.; Ma, Y.; Tang, J. RGB-T image saliency detection via collaborative graph learning. IEEE Transactions on Multimedia Vol. 22, No. 1, 160-173, 2020.
Crossref Google Scholar
[204]
Zhang, Q.; Huang, N. C.; Yao, L.; Zhang, D. W.; Shan, C. F.; Han, J. G. RGB-T salient object detection via fusing multi-level CNN features. IEEE Transactions on Image Processing Vol. 29, 3321-3335, 2020.
Crossref Google Scholar
[205]
Tu, Z. Z.; Ma, Y.; Li, Z.; Li, C. L.; Xu, J. M.; Liu, Y. T. RGBT salient object detection: A large-scale dataset and benchmark. arXiv preprint arXiv:2007.03262, 2020.
Google Scholar
Computational Visual Media
Volume 7 Issue 1,
March 2021
Pages 37-69
DOI: 10.1007/s41095-020-0199-z
Cite this article:
Zhou T, Fan D-P, Cheng M-M, et al. RGB-D salient object detection: A survey. Computational Visual Media, 2021, 7(1): 37-69. https://doi.org/10.1007/s41095-020-0199-z

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Received: 31 July 2020
Accepted: 07 October 2020
Published: 07 January 2021
© The Author(s) 2020

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