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The fully convolutional network (FCN) has dominated salient object detection for a long period.
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2017
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2017
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L. Wang, H. Lu, Y. Wang, M. Feng, D. Wang, B. Yin, and X. Ruan, “Learning to detect salient objects with image-level supervision,” in CVPR , 2017
2017
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2017
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2017
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2017
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2020
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2018
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L. Zhang, J. Dai, H. Lu, Y. He, and G. Wang, “A bi-directional message passing model for salient object detection,” in CVPR , 2018, pp. 1741–1750
2018
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2018
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2018
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2018
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T. Wang, L. Zhang, S. Wang, H. Lu, G. Yang, X. Ruan, and A. Borji, “Detect globally, refine locally: A novel approach to saliency detection,” in CVPR , 2018, pp. 3127–3135
2018
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X. Wang, R. Girshick, A. Gupta, and K. He, “Non-local neural networks,” in CVPR , 2018, pp. 7794–7803
2018
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S. Chen, X. Tan, B. Wang, and X. Hu, “Reverse attention for salient object detection,” in ECCV , 2018, pp. 234–250
2018
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2020
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2020
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2020
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2020
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2020
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J. Wei, S. Wang, Z. Wu, C. Su, Q. Huang, and Q. Tian, “Label decoupling framework for salient object detection,” in CVPR , June 2020
2020
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Y. Pang, X. Zhao, L. Zhang, and H. Lu, “Multi-scale interactive network for salient object detection,” in CVPR , June 2020
2020
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X. Hu, C.-W. Fu, L. Zhu, T. Wang, and P.-A. Heng, “Sac-net: Spatial attenuation context for salient object detection,” IEEE Transactions on Circuits and Systems for Video Technology , 2020, to appear
2020
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2021
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S. Ren, W. Liu, Y. Liu, H. Chen, G. Han, and S. He, “Reciprocal transformations for unsupervised video object segmentation,” in CVPR , June 2021, pp. 15 455–15 464
2021
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W. Wang, Q. Lai, H. Fu, J. Shen, H. Ling, and R. Yang, “Salient object detection in the deep learning era: An in-depth survey,” IEEE Transactions on Pattern Analysis and Machine Intelligence , vol. 44, no. 6, pp. 3239–3259, 2021
2021
Closest in time.
B. Xu, H. Liang, R. Liang, and P. Chen, “Locate globally, segment locally: A progressive architecture with knowledge review network for salient object detection,” in Proceedings of the AAAI Conference on Artificial Intelligence , vol. 35, no. 4, 2021, pp. 3004–3012
2021
Closest in time.
2021
Closest in time.
W. Wang, G. Sun, and L. Van Gool, “Looking beyond single images for weakly supervised semantic segmentation learning,” IEEE Transactions on Pattern Analysis and Machine Intelligence , 2022
2022
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T. Huang, X. Ben, C. Gong, B. Zhang, R. Yan, and Q. Wu, “Enhanced spatial-temporal salience for cross-view gait recognition,” IEEE Transactions on Circuits and Systems for Video Technology , vol. 32, no. 10, pp. 6967–6980, 2022
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2022
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Y. K. Yun and T. Tsubono, “Recursive contour-saliency blending network for accurate salient object detection,” in Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (WACV) , January 2022, pp. 2940–2950
2022
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S. Ren, W. Liu, J. Jiao, G. Han, and S. He, “Edge distraction-aware salient object detection,” IEEE MultiMedia , vol. 30, no. 3, pp. 63–73, 2023
2023
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S. Ren, F. Wei, Z. Zhang, and H. Hu, “Tinymim: An empirical study of distilling mim pre-trained models,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2023, pp. 3687–3697
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2023
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J.-J. Liu, Q. Hou, Z.-A. Liu, and M.-M. Cheng, “Poolnet+: Exploring the potential of pooling for salient object detection,” IEEE TPAMI , vol. 45, no. 1, pp. 887–904, 2023
2023
Closest in time.
C. Yao, L. Feng, Y. Kong, L. Xiao, and T. Chen, “Transformers and cnns fusion network for salient object detection,” Neurocomputing , vol. 520, pp. 342–355, 2023. [Online]. Available: https://www.sciencedirect.com/science/article/pii/S0925231222013704
2023
Closest in time.