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How to effectively fuse cross-modal information is the key problem for RGB-D salient object detection.
G. Lin, A. Milan, C. Shen, and I. Reid, “Refinenet: Multi-path refinement networks for high-resolution semantic segmentation,” in CVPR , 2017, pp. 1925–1934
1934
Earlier work this paper cites.
L. Itti, C. Koch, and E. Niebur, “A model of saliency-based visual attention for rapid scene analysis,” TPAMI , vol. 20, no. 11, pp. 1254–1259, 1998
1998
Earlier work this paper cites.
R. Ng, M. Levoy, M. Brédif, G. Duval, M. Horowitz, and P. Hanrahan, “Light field photography with a hand-held plenoptic camera,” Ph.D. dissertation, Stanford University, 2005
2005
Earlier work this paper cites.
J. Deng, W. Dong, R. Socher, L. J. Li, and F. F. Li, “Imagenet: A large-scale hierarchical image database,” in CVPR , 2009, pp. 248–255
2009
Earlier work this paper cites.
C. Liu, J. Yuen, and A. Torralba, “Sift flow: Dense correspondence across scenes and its applications,” TPAMI , vol. 33, no. 5, pp. 978–994, 2010
2010
Earlier work this paper cites.
D. Sun, S. Roth, and M. J. Black, “Secrets of optical flow estimation and their principles,” in CVPR . IEEE, 2010, pp. 2432–2439
2010
Earlier work this paper cites.
Y. Niu, Y. Geng, X. Li, and F. Liu, “Leveraging stereopsis for saliency analysis,” in CVPR . IEEE, 2012, pp. 454–461
2012
Earlier work this paper cites.
Z. Zhang, “Microsoft kinect sensor and its effect,” IEEE multimedia , vol. 19, no. 2, pp. 4–10, 2012
2012
Earlier work this paper cites.
K. Desingh, K. M. Krishna, D. Rajan, and C. Jawahar, “Depth really matters: Improving visual salient region detection with depth.” in BMVC , 2013
2013
Earlier work this paper cites.
A. Ciptadi, T. Hermans, and J. Rehg, “An in depth view of saliency,” in BMVC , 2013
2013
Earlier work this paper cites.
M. W. Tao, S. Hadap, J. Malik, and R. Ramamoorthi, “Depth from combining defocus and correspondence using light-field cameras,” in Proceedings of the IEEE International Conference on Computer Vision , 2013, pp. 673–680
2013
Earlier work this paper cites.
M.-M. Cheng, N. J. Mitra, X. Huang, P. H. Torr, and S.-M. Hu, “Global contrast based salient region detection,” IEEE transactions on pattern analysis and machine intelligence , vol. 37, no. 3, pp. 569–582, 2014
2014
Earlier work this paper cites.
N. Li, J. Ye, Y. Ji, H. Ling, and J. Yu, “Saliency detection on light field,” in CVPR , 2014, pp. 2806–2813
2014
Earlier work this paper cites.
Y. Cheng, H. Fu, X. Wei, J. Xiao, and X. Cao, “Depth enhanced saliency detection method,” in International Conference on Internet Multimedia Computing and Service . ACM, 2014, p. 23
2014
Earlier work this paper cites.
H. Peng, B. Li, W. Xiong, W. Hu, and R. Ji, “Rgbd salient object detection: A benchmark and algorithms,” in ECCV . Springer, 2014, pp. 92–109
2014
Earlier work this paper cites.
R. Ju, L. Ge, W. Geng, T. Ren, and G. Wu, “Depth saliency based on anisotropic center-surround difference,” in ICIP . IEEE, 2014, pp. 1115–1119
2014
Earlier work this paper cites.
O. Ronneberger, P. Fischer, and T. Brox, “U-net: Convolutional networks for biomedical image segmentation,” in MICCAI , 2015, pp. 234–241
2015
Earlier work this paper cites.
S. Xiang, L. Yu, and C. W. Chen, “No-reference depth assessment based on edge misalignment errors for t+ d images,” TIP , vol. 25, no. 3, pp. 1479–1494, 2015
2015
Earlier work this paper cites.
A. Borji, M.-M. Cheng, H. Jiang, and J. Li, “Salient object detection: A benchmark,” IEEE transactions on image processing , vol. 24, no. 12, pp. 5706–5722, 2015
2015
Earlier work this paper cites.
S. Ioffe and C. Szegedy, “Batch normalization: Accelerating deep network training by reducing internal covariate shift,” in ICML , 2015, pp. 448–456
2015
Earlier work this paper cites.
K. Simonyan and A. Zisserman, “Very deep convolutional networks for large-scale image recognition,” in ICLR , 2015
2015
Earlier work this paper cites.
J. Long, E. Shelhamer, and T. Darrell, “Fully convolutional networks for semantic segmentation,” in CVPR , 2015, pp. 3431–3440
2015
Earlier work this paper cites.
N. Liu and J. Han, “Dhsnet: Deep hierarchical saliency network for salient object detection,” in CVPR , 2016, pp. 678–686
2016
Earlier work this paper cites.
J. Guo, T. Ren, and J. Bei, “Salient object detection for rgb-d image via saliency evolution,” in ICME . IEEE, 2016, pp. 1–6
2016
Cited alongside, same era.
G. Li and Y. Yu, “Deep contrast learning for salient object detection,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition , 2016, pp. 478–487
2016
Cited alongside, same era.
R. Cong, J. Lei, C. Zhang, Q. Huang, and C. Hou, “Saliency detection for stereoscopic images based on depth confidence analysis and multiple cues fusion,” IEEE Signal Processing Letters , vol. 23, no. 6, pp. 819–823, 2016
2016
Cited alongside, same era.
K. He, X. Zhang, S. Ren, and J. Sun, “Deep residual learning for image recognition,” in CVPR , 2016, pp. 770–778
2016
Cited alongside, same era.
H. Song, Z. Liu, H. Du, G. Sun, O. Le Meur, and T. Ren, “Depth-aware salient object detection and segmentation via multiscale discriminative saliency fusion and bootstrap learning,” TIP , vol. 26, no. 9, pp. 4204–4216, 2017
M. Yang, K. Yu, C. Zhang, Z. Li, and K. Yang, “Denseaspp for semantic segmentation in street scenes,” in CVPR , 2018, pp. 3684–3692
2018
Later among the works it cites.
D.-P. Fan, C. Gong, Y. Cao, B. Ren, M.-M. Cheng, and A. Borji, “Enhanced-alignment measure for binary foreground map evaluation,” in IJCAI . AAAI Press, 2018, pp. 698–704
2018
Later among the works it cites.
W. Wang, J. Shen, M.-M. Cheng, and L. Shao, “An iterative and cooperative top-down and bottom-up inference network for salient object detection,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition , 2019, pp. 5968–5977
2019
Later among the works it cites.
J.-X. Zhao, J.-J. Liu, D.-P. Fan, Y. Cao, J. Yang, and M.-M. Cheng, “Egnet: Edge guidance network for salient object detection,” in Proceedings of the IEEE International Conference on Computer Vision , 2019, pp. 8779–8788
2019
Later among the works it cites.
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2017
Cited alongside, same era.
L. Qu, S. He, J. Zhang, J. Tian, Y. Tang, and Q. Yang, “Rgbd salient object detection via deep fusion,” TIP , vol. 26, no. 5, pp. 2274–2285, 2017
2017
Cited alongside, same era.
R. Shigematsu, D. Feng, S. You, and N. Barnes, “Learning rgb-d salient object detection using background enclosure, depth contrast, and top-down features,” in ICCV Workshops , 2017, pp. 2749–2757
2017
Cited alongside, same era.
J. Han, H. Chen, N. Liu, C. Yan, and X. Li, “Cnns-based rgb-d saliency detection via cross-view transfer and multiview fusion,” IEEE Transactions on Cybernetics , vol. 48, no. 11, pp. 3171–3183, 2017
2017
Cited alongside, same era.
C. Zhu and G. Li, “A three-pathway psychobiological framework of salient object detection using stereoscopic technology,” in ICCV Workshops , 2017, pp. 3008–3014
2017
Cited alongside, same era.
A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A. N. Gomez, Ł. Kaiser, and I. Polosukhin, “Attention is all you need,” in NIPS , 2017, pp. 5998–6008
2017
Cited alongside, same era.
H. Nam, J.-W. Ha, and J. Kim, “Dual attention networks for multimodal reasoning and matching,” in CVPR , 2017, pp. 299–307
2017
Cited alongside, same era.
E. Ilg, N. Mayer, T. Saikia, M. Keuper, A. Dosovitskiy, and T. Brox, “Flownet 2.0: Evolution of optical flow estimation with deep networks,” in CVPR , 2017, pp. 2462–2470
2017
Cited alongside, same era.
X. Qin, Z. Zhang, C. Huang, C. Gao, M. Dehghan, and M. Jagersand, “Basnet: Boundary-aware salient object detection,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition , 2019, pp. 7479–7489
2019
Later among the works it cites.
Z. Liu, S. Shi, Q. Duan, W. Zhang, and P. Zhao, “Salient object detection for rgb-d image by single stream recurrent convolution neural network,” Neurocomputing , vol. 363, pp. 46–57, 2019
2019
Later among the works it cites.
2019
Later among the works it cites.
N. Wang and X. Gong, “Adaptive fusion for rgb-d salient object detection,” IEEE Access , vol. 7, pp. 55 277–55 284, 2019
2019
Later among the works it cites.
Y. Piao, W. Ji, J. Li, M. Zhang, and H. Lu, “Depth-induced multi-scale recurrent attention network for saliency detection,” in ICCV , 2019
2019
Later among the works it cites.
H. Chen, Y. Li, and D. Su, “Multi-modal fusion network with multi-scale multi-path and cross-modal interactions for rgb-d salient object detection,” Pattern Recognition , vol. 86, pp. 376–385, 2019
2019
Later among the works it cites.
H. Chen and Y. Li, “Three-stream attention-aware network for rgb-d salient object detection,” TIP , vol. 28, no. 6, pp. 2825–2835, 2019
2019
Later among the works it cites.
J.-X. Zhao, Y. Cao, D.-P. Fan, M.-M. Cheng, X.-Y. Li, and L. Zhang, “Contrast prior and fluid pyramid integration for rgbd salient object detection,” in CVPR , 2019
2019
Later among the works it cites.
Y. Wan, J. Shu, Y. Sui, G. Xu, Z. Zhao, J. Wu, and P. Yu, “Multi-modal attention network learning for semantic source code retrieval,” in IEEE/ACM International Conference on Automated Software Engineering . IEEE, 2019, pp. 13–25
2019
Later among the works it cites.
Z. Zhang, Z. Cui, C. Xu, Y. Yan, N. Sebe, and J. Yang, “Pattern-affinitive propagation across depth, surface normal and semantic segmentation,” in CVPR , 2019, pp. 4106–4115
2019
Later among the works it cites.
H. Touvron, A. Vedaldi, M. Douze, and H. Jégou, “Fixing the train-test resolution discrepancy,” pp. 8252–8262, 2019
2019
Later among the works it cites.
N. Liu, J. Han, and M.-H. Yang, “Picanet: Pixel-wise contextual attention learning for accurate saliency detection,” IEEE Transactions on Image Processing , 2020
2020
Closest in time.
K. Fu, D.-P. Fan, G.-P. Ji, and Q. Zhao, “Jl-dcf: Joint learning and densely-cooperative fusion framework for rgb-d salient object detection,” in CVPR , 2020
2020
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M. Zhang, W. Ren, Y. Piao, Z. Rong, and H. Lu, “Select, supplement and focus for rgb-d saliency detection,” in CVPR , 2020, pp. 3472–3481
2020
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N. Liu, N. Zhang, and J. Han, “Learning selective self-mutual attention for rgb-d saliency detection,” in CVPR , 2020, pp. 13 756–13 765
2020
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G. Li, Z. Liu, and H. Ling, “Icnet: Information conversion network for rgb-d based salient object detection,” TIP , vol. 29, pp. 4873–4884, 2020
2020
Closest in time.
H. Zhou, X. Xie, J.-H. Lai, Z. Chen, and L. Yang, “Interactive two-stream decoder for accurate and fast saliency detection,” in CVPR , 2020, pp. 9141–915
2020
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Y. Piao, Z. Rong, M. Zhang, W. Ren, and H. Lu, “A2dele: Adaptive and attentive depth distiller for efficient rgb-d salient object detection,” in CVPR , 2020, pp. 9060–9069
2020
Closest in time.