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Delving into Calibrated Depth for Accurate RGB-D Salient Object Detection

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Abstract

Recent years have witnessed growing interests in RGB-D Salient Object Detection (SOD), benefiting from the ample spatial layout cues embedded in depth maps to help SOD models distinguish salient objects from complex backgrounds or similar surroundings. Despite these progresses, this emerging line of research has been considerably hindered by the noise and ambiguity that prevail in raw depth images, as well as the coarse object boundaries in saliency predictions. To address the aforementioned issues, we propose a Depth Calibration and Boundary-aware Fusion (DCBF) framework that contains two novel components: (1) a learning strategy to calibrate the latent bias in the original depth maps towards boosting the SOD performance; (2) a boundary-aware multimodal fusion module to fuse the complementary cues from RGB and depth channels, as well as to improve object boundary qualities. In addition, we introduce a new saliency dataset, HiBo-UA, which contains 1515 high-resolution RGB-D images with finely-annotated pixel-level labels. To our best knowledge, this is the first RGB-D-based high-resolution saliency dataset with significantly higher image resolution (nearly 7\(\times \)) than the widely used DUT-D dataset. The proposed high-resolution dataset with richer object boundary details is capable of accurately assessing the performance of various saliency models, in order to retain fine-grained object boundaries. It also facilitates the growing need of our research community in accessing higher-resolution data. Extensive empirical experiments demonstrate the superior performance of our approach against 31 state-of-the-art methods. It is worth noting that our calibrated depth alone can work in a plug-and-play manner; empirically it is shown to bring noticeable improvements when applied to existing state-of-the-art RGB-D SOD models.

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Notes

  1. The source code and proposed HiBo-UA dataset will be available at https://github.com/jiwei0921/HiBo-UA.

References

  • Achanta, R., Hemami, S., Estrada, F., & Susstrunk, S. (2009). Frequency-tuned salient region detection. In CVPR (pp. 1597–1604).

  • Allison, R. S., Gillam, B. J., & Vecellio, E. (2009). Binocular depth discrimination and estimation beyond interaction space. Journal of Vision, 9(1), 10–10.

    Article  Google Scholar 

  • Bhat, D. N., & Nayar, S. K. (1995). Stereo in the presence of specular reflection. In ICCV (pp. 1086–1092).

  • Borji, A., Cheng, M. M., Jiang, H., & Li, J. (2015). Salient object detection: A benchmark. IEEE Transactions on Image Processing, 24(12), 5706–5722.

    Article  MathSciNet  MATH  Google Scholar 

  • Canny, J. (1986). A computational approach to edge detection. IEEE Transactions on Pattern Analysis and Machine Intelligence, 8(6), 679–698.

    Article  Google Scholar 

  • Chen, C., Wei, J., Peng, C., Zhang, W., & Qin, H. (2020). Improved saliency detection in RGB-D images using two-phase depth estimation and selective deep fusion. IEEE Transactions on Image Processing, 29, 4296–4307.

    Article  MATH  Google Scholar 

  • Chen, C., Wei, J., Peng, C., & Qin, H. (2021). Depth-quality-aware salient object detection. IEEE Transactions on Image Processing, 30, 2350–2363.

    Article  Google Scholar 

  • Chen, H., & Li, Y. (2018). Progressively complementarity-aware fusion network for RGB-D salient object detection. In CVPR (pp. 3051–3060).

  • Chen, H., & Li, Y. (2019). Three-stream attention-aware network for RGB-D salient object detection. IEEE Transactions on Image Processing, 28(6), 2825–2835.

    Article  MathSciNet  MATH  Google Scholar 

  • Chen, H., Li, Y., & Su, D. (2019). Multi-modal fusion network with multi-scale multi-path and cross-modal interactions for RGB-D salient object detection. Pattern Recognition, 86, 376–385.

    Article  Google Scholar 

  • Chen, H., Deng, Y., Li, Y., Hung, T. Y., & Lin, G. (2020). RGBD salient object detection via disentangled cross-modal fusion. IEEE Transactions on Image Processing, 29, 8407–8416.

    Article  MATH  Google Scholar 

  • Chen, H., Li, Y., Deng, Y., & Lin, G. (2021). Cnn-based rgb-d salient object detection: Learn, select, and fuse. International Journal of Computer Vision, 6, 1–21.

    Google Scholar 

  • Chen, L. C., Papandreou, G., Kokkinos, I., Murphy, K., & Yuille, A. L. (2017). Deeplab: Semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected crfs. IEEE Transactions on Pattern Analysis and Machine Intelligence, 40(4), 834–848.

    Article  Google Scholar 

  • Chen, Q., Liu, Z., Zhang, Y., Fu, K., Zhao, Q., & Du, H. (2021c). RGB-D salient object detection via 3D convolutional neural. In AAAI.

  • Chen, S., & Fu, Y. (2020). Progressively guided alternate refinement network for RGB-D salient object detection. In ECCV.

  • Chen, W., Yu, S., Ma, K., Ji, W., Bian, C., Chu, C., et al. (2022). Tw-gan: Topology and width aware gan for retinal artery/vein classification. Medical Image Analysis, 77, 102340.

    Article  Google Scholar 

  • Chen, Z., Cong, R., Xu, Q., & Huang, Q. (2020). Depth potentiality-aware gated attention network for RGB-D salient object detection. IEEE Transactions on Image Processing, 3, 996.

    Google Scholar 

  • Cheng, Y., Fu, H., Wei, X., Xiao, J., & Cao, X. (2014). Depth enhanced saliency detection method. In Proceedings of International Conference on Internet Multimedia Computing and Service (pp. 23–27).

  • Cong, R., Lei, J., Zhang, C., Huang, Q., Cao, X., & Hou, C. (2016). Saliency detection for stereoscopic images based on depth confidence analysis and multiple cues fusion. IEEE Signal Processing Letters, 23(6), 819–823.

    Article  Google Scholar 

  • Cong, R., Lei, J., Fu, H., Cheng, M. M., Lin, W., & Huang, Q. (2018). Review of visual saliency detection with comprehensive information. IEEE Transactions on circuits and Systems for Video Technology, 29(10), 2941–2959.

    Article  Google Scholar 

  • Cong, R., Lei, J., Fu, H., Huang, Q., Cao, X., & Hou, C. (2018). Co-saliency detection for RGBD images based on multi-constraint feature matching and cross label propagation. IEEE Transactions on Image Processing, 27(2), 568–579.

    Article  MathSciNet  MATH  Google Scholar 

  • Cong, R., Lei, J., Fu, H., Huang, Q., Cao, X., & Ling, N. (2018). HSCS: Hierarchical sparsity based co-saliency detection for RGBD images. IEEE Transactions on Multimedia, 21(7), 1660–1671.

    Article  Google Scholar 

  • Cong, R., Lei, J., Fu, H., Lin, W., Huang, Q., Cao, X., & Hou, C. (2019). An iterative co-saliency framework for RGBD images. IEEE Transactions on Cybernetics, 49(1), 233–246.

    Article  Google Scholar 

  • Cong, R., Lei, J., Fu, H., Hou, J., Huang, Q., & Kwong, S. (2020). Going from RGB to RGBD saliency: A depth-guided transformation model. IEEE Transactions on Systems, Man, and Cybernetics, 50(8), 3627–3639.

    Google Scholar 

  • Desingh, K., Krishna, K. M., Rajan, D., & Jawahar, C. (2013). Depth really matters: Improving visual salient region detection with depth. In BMVC.

  • Fan, DP., Cheng, MM., Liu, JJ., Gao, S. H., Hou, Q., & Borji, A. (2018a). Salient objects in clutter: Bringing salient object detection to the foreground. In ECCV (pp. 186–202).

  • Fan, D. P., Gong, C., Cao, Y., Ren, B., Cheng, M. M., & Borji, A. (2018b). Enhanced-alignment measure for binary foreground map evaluation. In IJCAI (pp. 698–704).

  • Fan, D. P., Lin, Z., Zhang, Z., Zhu, M., & Cheng, M. M. (2020). Rethinking RGB-D salient object detection: Models, data sets, and large-scale benchmarks. IEEE Transactions on Neural Networks and Learning Systems, 6, 966.

    Google Scholar 

  • Fan, D. P., Zhai, Y., Borji, A., Yang, J., & Shao, L. (2020b). BBS-Net: RGB-D salient object detection with a bifurcated backbone strategy network. In ECCV.

  • Fang, H., Gupta, S., Iandola, F., Srivastava, R. K., Deng, L., Dollár, P., Gao, J., He, X., Mitchell, M., Platt, J. C., & et al. (2015). From captions to visual concepts and back. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 1473–1482).

  • Feng, D., Barnes, N., You, S., & McCarthy, C. (2016). Local background enclosure for RGB-D salient object detection. In CVPR (pp. 2343–2350).

  • Feng, M., Lu, H., & Ding, E. (2019). Attentive feedback network for boundary-aware salient object detection. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition (pp. 1623–1632).

  • Fu, K., Fan, D. P., Ji, G. P., & Zhao, Q. (2020). JL-DCF: Joint learning and densely-cooperative fusion framework for RGB-D salient object detection. In CVPR (pp. 3052–3062).

  • Giancola, S., Valenti, M., & Sala, R. (2018). A Survey on 3D Cameras: Metrological Comparison of Time-of-Flight. Structured-Light and Active Stereoscopy Technologies. Springer.

    Book  Google Scholar 

  • Guo, C., Li, C., Guo, J., Loy, C. C., Hou, J., Kwong, S., & Cong, R. (2020). Zero-reference deep curve estimation for low-light image enhancement. In CVPR (pp. 1780–1789).

  • Han, J., Chen, H., Liu, N., Yan, C., & Li, X. (2017). CNNs-based RGB-D saliency detection via cross-view transfer and multiview fusion. IEEE Transactions on Cybernetics, 48(11), 3171–3183.

    Article  Google Scholar 

  • He, K., Zhang, X., Ren, S., & Sun, J. (2016). Deep residual learning for image recognition. In CVPR (pp. 770–778).

  • Hou, Q., Cheng, M. M., Hu, X., Borji, A., Tu, Z., & Torr, P. H. (2017). Deeply supervised salient object detection with short connections. In CVPR (pp. 3203–3212).

  • Ji, W., Li, J., Zhang, M., Piao, Y., & Lu, H. (2020). Accurate RGB-D salient object detection via collaborative learning. In ECCV.

  • Ji, W., Li, J., Yu, S., Zhang, M., Piao, Y., Yao, S., Bi, Q., Ma, K., Zheng, Y., Lu, H., & Cheng, L. (2021a). Calibrated RGB-D salient object detection. In CVPR (pp. 9471–9481).

  • Ji, W., Yu, S., Wu, J., Ma, K., Bian, C., Bi, Q., Li, J., Liu, H., Cheng, L., & Zheng, Y. (2021b). Learning calibrated medical image segmentation via multi-rater agreement modeling. In CVPR (pp. 12341–12351).

  • Ji, W., Li, J., Bi, Q., Guo, C., Liu, J., & Cheng, L. (2022a). Promoting saliency from depth: Deep unsupervised rgb-d saliency detection. In International conference on learning representations.

  • Ji, W., Yan, G., Li, J., Piao, Y., Yao, S., Zhang, M., et al. (2022). Dmra: Depth-induced multi-scale recurrent attention network for rgb-d saliency detection. IEEE Transactions on Image Processing, 31, 2321–2336.

    Article  Google Scholar 

  • Jiang, Y., Zhou, T., Ji, G. P., Fu, K., Zhao, Q., & Fan, D. P. (2020). Light field salient object detection: A review and benchmark. arXiv preprint arXiv:2010.04968

  • Ju, R., Ge, L., Geng, W., Ren, T., & Wu, G. (2014). Depth saliency based on anisotropic center-surround difference. In ICIP (pp. 1115–1119).

  • Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2012). ImageNet classification with deep convolutional neural networks. In NeurIPS (pp. 1097–1105).

  • Li, C., Cong, R., Hou, J., Zhang, S., Qian, Y., & Kwong, S. (2019). Nested network with two-stream pyramid for salient object detection in optical remote sensing images. IEEE Transactions on Geoscience and Remote Sensing, 57(11), 9156–9166.

    Article  Google Scholar 

  • Li, C., Cong, R., Kwong, S., Hou, J., Zhu, G. F. H., Zhang, D., & Huang, Q. (2020). ASIF-Net: Attention steered interweave fusion network for RGB-D salient object detection. IEEE Transactions on Cybernetics, 51(1), 88–100.

    Article  Google Scholar 

  • Li, C., Cong, R., Piao, Y., Xu, Q., & Loy, C. C. (2020b). RGB-D salient object detection with cross-modality modulation and selection. In ECCV.

  • Li, G., & Yu, Y. (2015). Visual saliency based on multiscale deep features. In CVPR (pp. 5455–5463).

  • Li, G., Liu, Z., & Ling, H. (2020). Icnet: Information conversion network for rgb-d based salient object detection. IEEE Transactions on Image Processing, 29, 4873–4884.

    Article  MATH  Google Scholar 

  • Li, G., Liu, Z., Ye, L., Wang, Y., & Ling, H. (2020d). Cross-modal weighting network for RGB-D salient object detection. In ECCV.

  • Li, G., Liu, Z., Chen, M., Bai, Z., Lin, W., & Ling, H. (2021). Hierarchical alternate interaction network for RGB-D salient object detection. IEEE Transactions on Image Processing, 5, 966.

    Google Scholar 

  • Li, J., Ji, W., Bi, Q., Yan, C., Zhang, M., Piao, Y., et al. (2021). Joint semantic mining for weakly supervised rgb-d salient object detection. NeurIPS, 34, 11945–11959.

    Google Scholar 

  • Li, N., Ye, J., Ji, Y., Ling, H., & Yu, J. (2017). Saliency detection on light field. IEEE Transactions on Pattern Analysis and Machine Intelligence, 39(8), 1605–1616.

    Article  Google Scholar 

  • Liu, J. J., Hou, Q., Cheng, M. M., Feng, J., & Jiang, J. (2019). A simple pooling-based design for real-time salient object detection. In CVPR (pp. 3917–3926).

  • Liu, J. J., Hou, Q., Liu, Z. A., & Cheng, M. M. (2022). Poolnet+: Exploring the potential of pooling for salient object detection. IEEE Transactions on Pattern Analysis and Machine Intelligence, 6, 8440.

    Google Scholar 

  • Liu, N., Zhang, N., & Han, J. (2020). Learning selective self-mutual attention for RGB-D saliency detection. In CVPR (pp. 13756–13765).

  • Liu, N., Li, L., Zhao, W., Han, J., & Shao, L. (2021). Instance-level relative saliency ranking with graph reasoning. IEEE Transactions on Pattern Analysis and Machine Intelligence, 7, 6003.

    Google Scholar 

  • Liu, N., Zhang, N., Shao, L., & Han, J. (2021). Learning selective mutual attention and contrast for RGB-D saliency detection. IEEE Transactions on Pattern Analysis and Machine Intelligence. https://doi.org/10.1109/TPAMI.2021.3122139.

    Article  Google Scholar 

  • Liu, N., Zhang, N., Wan, K., Shao, L., & Han, J. (2021c). Visual saliency transformer. In ICCV (pp. 4722–4732).

  • Liu, N., Zhao, W., Shao, L., & Han, J. (2021). Scg: Saliency and contour guided salient instance segmentation. IEEE Transactions on Image Processing, 2, 449.

    Google Scholar 

  • Luo, A., Li, X., Yang, F., Jiao, Z., Cheng, H., & Lyu, S. (2020). Cascade graph neural networks for RGB-D salient object detection. In ECCV.

  • Margolin, R., Zelnik-Manor, L., & Tal, A. (2014). How to evaluate foreground maps? In CVPR (pp. 248–255).

  • Niu, Y., Geng, Y., Li, X., & Liu, F. (2012). Leveraging stereopsis for saliency analysis. In CVPR (pp. 454–461).

  • Pang, Y., Zhang, L., Zhao, X., & Lu, H. (2020a). Hierarchical dynamic filtering network for RGB-D salient object detection. In ECCV.

  • Pang, Y., Zhao, X., Zhang, L., & Lu, H. (2020b). Multi-scale interactive network for salient object detection. In CVPR (pp. 9413–9422).

  • Patterson, R., Moe, L., & Hewitt, T. (1992). Factors that affect depth perception in stereoscopic displays. Human Factors, 34(6), 655–667.

    Article  Google Scholar 

  • Peng, H., Li, B., Xiong, W., Hu, W., & Ji, R. (2014). RGBD salient object detection: a benchmark and algorithms. In ECCV (pp. 92–109).

  • Perazzi, F., Pont-Tuset, J., McWilliams, B., Van Gool, L., Gross, M., & Sorkine-Hornung, A. (2016). A benchmark dataset and evaluation methodology for video object segmentation. In CVPR (pp. 724–732).

  • Piao, Y., Ji, W., Li, J., Zhang, M., & Lu, H. (2019). Depth-induced multi-scale recurrent attention network for saliency detection. In ICCV (pp. 7254–7263).

  • Piao, Y., Rong, Z., Zhang, M., Ren, W., & Lu, H. (2020). A2dele: Adaptive and attentive depth distiller for efficient RGB-D salient object detection. In CVPR (pp. 9060–9069).

  • Qin, X., Zhang, Z., Huang, C., Gao, C., Dehghan, M., & Jagersand, M. (2019). BASNet: Boundary-aware salient object detection. In CVPR (pp. 7479–7489).

  • Qu, L., He, S., Zhang, J., Tian, J., Tang, Y., & Yang, Q. (2017). RGBD salient object detection via deep fusion. IEEE Transactions on Image Processing, 26(5), 2274–2285.

    Article  MathSciNet  MATH  Google Scholar 

  • Ren, J., Gong, X., Yu, L., Zhou, W., & Ying Yang, M. (2015). Exploiting global priors for RGB-D saliency detection. In CVPR Workshops (pp. 25–32).

  • Ren, Z., Gao, S., Chia, L. T., & Tsang, I. W. H. (2013). Region-based saliency detection and its application in object recognition. IEEE Transactions on Circuits and Systems for Video Technology, 24(5), 769–779.

    Article  Google Scholar 

  • Shigematsu, R., Feng, D., You, S., & Barnes, N. (2017). Learning RGB-D salient object detection using background enclosure, depth contrast, and top-down features. In ICCV Workshops (pp. 2749–2757).

  • Song, H., Liu, Z., Du, H., Sun, G., Le Meur, O., & Ren, T. (2017). Depth-aware salient object detection and segmentation via multiscale discriminative saliency fusion and bootstrap learning. IEEE Transactions on Image Processing, 26(9), 4204–4216.

    Article  MathSciNet  MATH  Google Scholar 

  • Sun, P., Zhang, W., Wang, H., Li, S., & Li, X. (2021). Deep RGB-D saliency detection with depth-sensitive attention and automatic multi-modal fusion. In CVPR (pp. 1407–1417).

  • Tang, L., Li, B., Ding, S., & Song, M. (2021). Disentangled high quality salient object detection. In ICCV.

  • Wei, J., Wang, S., Wu, Z., Su, C., Huang, Q., & Tian, Q. (2020). Label decoupling framework for salient object detection. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition (pp. 13025–13034).

  • Williem, W., & Kyu Park, I. (2016). Robust light field depth estimation for noisy scene with occlusion. In CVPR (pp. 4396–4404).

  • Wu, Y. H., Liu, Y., Xu, J., Bian, J. W., Gu, Y. C., & Cheng, M. M. (2021). Mobilesal: Extremely efficient rgb-d salient object detection. IEEE Transactions on Pattern Analysis and Machine Intelligence, 6, 448.

    Google Scholar 

  • Wu, Z., Su, L., & Huang, Q. (2019a). Cascaded partial decoder for fast and accurate salient object detection. In CVPR (pp. 3907–3916).

  • Wu, Z., Su, L., & Huang, Q. (2019b). Stacked cross refinement network for edge-aware salient object detection. In ICCV (pp. 7264–7273).

  • Zeng, Y., Zhang, P., Zhang, J., Lin, Z., & Lu, H. (2019). Towards high-resolution salient object detection. In ICCV.

  • Zhang, C., Cong, R., Lin, Q., Ma, L., Li, F., Zhao, Y., Kwong, S. (2021a). Cross-modality discrepant interaction network for rgb-d salient object detection. In Proceedings of the 29th ACM international conference on multimedia (pp. 2094–2102).

  • Zhang, J., Fan, D. P., Dai, Y., Anwar, S., Saleh, F. S., Zhang, T., & Barnes, N. (2020a). UC-Net: Uncertainty inspired RGB-D saliency detection via conditional variational autoencoders. In CVPR (pp. 8582–8591).

  • Zhang, J., Fan, D. P., Dai, Y., Anwar, S., Saleh, F., Aliakbarian, S., & Barnes, N. (2021). Uncertainty inspired RGB-D saliency detection. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2, 7008.

    Google Scholar 

  • Zhang, K., Xie, J., Snavely, N., & Chen, Q. (2020b). Depth sensing beyond LiDAR range. In CVPR (pp. 1692–1700).

  • Zhang, M., Li, J., Ji, W., Piao, Y., & Lu, H. (2019). Memory-oriented decoder for light field salient object detection. In NeurIPS (pp. 896–906).

  • Zhang, M., Fei, S. X., Liu, J., Xu, S., Piao, Y., & Lu, H. (2020c). Asymmetric two-stream architecture for accurate RGB-D saliency detection. In ECCV.

  • Zhang, M., Ji, W., Piao, Y., Li, J., Zhang, Y., Xu, S., & Lu, H. (2020). LFNet: Light field fusion network for salient object detection. IEEE Transactions on Image Processing, 29, 6276–6287.

    Article  MATH  Google Scholar 

  • Zhang, M., Ren, W., Piao, Y., Rong, Z., & Lu, H. (2020e). Select, supplement and focus for RGB-D saliency detection. In CVPR (pp. 3472–3481).

  • Zhang, M., Zhang, Y., Piao, Y., Hu, B., & Lu, H. (2020f). Feature reintegration over differential treatment: A top-down and adaptive fusion network for RGB-D salient object detection. In ACMM.

  • Zhang, M., Liu, J., Wang, Y., Piao, Y., Yao, S., Ji, W., Li, J., Lu, H., & Luo, Z. (2021c). Dynamic context-sensitive filtering network for video salient object detection. In ICCV (pp. 1553–1563).

  • Zhang, M., Yao, S., Hu, B., Piao, Y., & Ji, W. (2022). \(\text{ C}^{2}\)dfnet: Criss-cross dynamic filter network for rgb-d salient object detection. IEEE Transactions on Multimedia, 6, 788.

    Google Scholar 

  • Zhang, Q., Cong, R., Hou, J., Li, C., & Zhao, Y. (2020g). CoADNet: Collaborative aggregation-and-distribution networks for co-salient object detection. In NeurIPS.

  • Zhang, W., Ji, G. P., Wang, Z., Fu, K., & Zhao, Q. (2021d). Depth quality-inspired feature manipulation for efficient rgb-d salient object detection. In ACMM (pp. 731–740).

  • Zhang, Z. (2012). Microsoft Kinect sensor and its effect. IEEE Multimedia, 19(2), 4–10.

    Article  Google Scholar 

  • Zhao, J., Zhao, Y., Li, J., & Chen, X. (2020a). Is depth really necessary for salient object detection? In ACMM.

  • Zhao, J. X., Cao, Y., Fan, D. P., Cheng, M. M., Li, X. Y., & Zhang, L. (2019a). Contrast prior and fluid pyramid integration for RGBD salient object detection. In CVPR (pp. 3927–3936).

  • Zhao, J. X., Liu, J. J., Fan, D. P., Cao, Y., Yang, J., & Cheng, M. M. (2019b). EGNet: Edge guidance network for salient object detection. In ICCV (pp. 8779–8788).

  • Zhao, S., Fu, H., Gong, M., & Tao, D. (2019c). Geometry-aware symmetric domain adaptation for monocular depth estimation. In CVPR (pp. 9788–9798).

  • Zhao, X., Pang, Y., Zhang, L., Lu, H., & Zhang, L. (2020b). Suppress and balance: A simple gated network for salient object detection. In ECCV (pp. 35–51).

  • Zhao, X., Zhang, L., Pang, Y., Lu, H., & Zhang, L. (2020c). A single stream network for robust and real-time RGB-D salient object detection. In ECCV.

  • Zhao, X., Pang, Y., Yang, J., Zhang, L., & Lu, H. (2021a). Multi-source fusion and automatic predictor selection for zero-shot video object segmentation. In ACM MM (pp. 2645–2653).

  • Zhao, X., Pang, Y., Zhang, L., Lu, H., & Ruan, X. (2022). Self-supervised pretraining for rgb-d salient object detection. AAAI Conference on Artificial Intelligence, 3, 966.

    Google Scholar 

  • Zhao, Y., Zhao, J., Li, J., & Chen, X. (2021). Rgb-d salient object detection with ubiquitous target awareness. IEEE Transactions on Image Processing, 30, 7717–7731.

    Article  Google Scholar 

  • Zhou, T., Fan, D. P., Cheng, M. M., Shen, J., & Shao, L. (2021). RGB-D salient object detection: A survey. Computational Visual Media, 2, 1–33.

    Google Scholar 

  • Zhu, C., Li, G., Guo, X., Wang, W., & Wang, R. (2017a). A multilayer backpropagation saliency detection algorithm based on depth mining. In CAIP (pp. 14–23).

  • Zhu, C., Li, G., Wang, W., & Wang, R. (2017b). An innovative salient object detection using center-dark channel prior. In ICCV Workshops (pp. 1509–1515).

  • Zhu, C., Cai, X., Huang, K., Li, T. H., & Li, G. (2019). PDNet: Prior-model guided depth-enhanced network for salient object detection. In ICME (pp. 199–204).

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Acknowledgements

This work was partly supported by the NSERC Discovery, UAHJIC, Mitacs, and CFI-JELF grants.

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  1. Department of Electrical and Computer Engineering, University of Alberta, Edmonton, Canada

    Jingjing Li, Wei Ji & Li Cheng

  2. Dalian University of Technology, Dalian, China

    Miao Zhang, Yongri Piao & Huchuan Lu

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  1. Jingjing Li
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Li, J., Ji, W., Zhang, M. et al. Delving into Calibrated Depth for Accurate RGB-D Salient Object Detection. Int J Comput Vis 131, 855–876 (2023). https://doi.org/10.1007/s11263-022-01734-1

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