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Camouflaged objects are typically assimilated into their backgrounds and exhibit fuzzy boundaries.
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D. Fan, T. Zhou, G. Ji, Y. Zhou, G. Chen, H. Fu, J. Shen, and L. Shao, “Inf-net: Automatic COVID-19 lung infection segmentation from CT images,” IEEE Trans. Medical Imaging , vol. 39, no. 8, pp. 2626–2637, 2020
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2020
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Cited alongside, same era.
X. Qin, Z. V. Zhang, C. Huang, M. Dehghan, O. R. Zaïane, and M. Jägersand, “U 2 {}^{\mbox{2}} -net: Going deeper with nested u-structure for salient object detection,” Pattern Recognit. , vol. 106, p. 107404, 2020
2020
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J. Wei, S. Wang, and Q. Huang, “F 3 net: Fusion, feedback and focus for salient object detection,” in AAAI , 2020, pp. 12 321–12 328
2020
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M. Feng, H. Lu, and Y. Yu, “Residual learning for salient object detection,” IEEE Transactions on Image Processing , vol. 29, pp. 4696–4708, 2020
2020
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J. R. Hall, O. Matthews, T. N. Volonakis, E. Liggins, K. P. Lymer, R. Baddeley, I. C. Cuthill, and N. E. Scott-Samuel, “A platform for initial testing of multiple camouflage patterns,” Defence Technology , vol. 17, no. 6, pp. 1833–1839, 2021
2021
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J. Yan, T. Le, K. Nguyen, M. Tran, T. Do, and T. V. Nguyen, “Mirrornet: Bio-inspired camouflaged object segmentation,” IEEE Access , vol. 9, pp. 43 290–43 300, 2021
2021
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2021
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H. Mei, G. Ji, Z. Wei, X. Yang, X. Wei, and D. Fan, “Camouflaged object segmentation with distraction mining,” in CVPR , 2021, pp. 8772–8781
2021
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2021
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F. Yang, Q. Zhai, X. Li, R. Huang, A. Luo, H. Cheng, and D.-P. Fan, “Uncertainty-guided transformer reasoning for camouflaged object detection,” in ICCV , 2021, pp. 4126–4135
2021
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T. Zhou, Y. Zhou, C. Gong, J. Yang, and Y. Zhang, “Feature aggregation and propagation network for camouflaged object detection,” IEEE Transactions on Image Processing , vol. 31, pp. 7036–7047, 2022
2022
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2022
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2022
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Y. Sun, S. Wang, C. Chen, and T. Xiang, “Boundary-guided camouflaged object detection,” in IJCAI , 2022, pp. 1335–1341
2022
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2022
Later among the works it cites.
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2022
Later among the works it cites.
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2022
Later among the works it cites.
P. Li, X. Yan, H. Zhu, M. Wei, X.-P. Zhang, and J. Qin, “Findnet: Can you find me? boundary-and-texture enhancement network for camouflaged object detection,” IEEE Transactions on Image Processing , vol. 31, pp. 6396–6411, 2022
2022
Later among the works it cites.
C. He, L. Xu, and Z. Qiu, “Eldnet: Establishment and refinement of edge likelihood distributions for camouflaged object detection,” in ICIP , 2022, pp. 621–625
2022
Later among the works it cites.
Q. Jia, S. Yao, Y. Liu, X. Fan, R. Liu, and Z. Luo, “Segment, magnify and reiterate: Detecting camouflaged objects the hard way,” in CVPR , 2022, pp. 4703–4712
2022
Later among the works it cites.
H. Zhu, P. Li, H. Xie, X. Yan, D. Liang, D. Chen, M. Wei, and J. Qin, “I can find you! boundary-guided separated attention network for camouflaged object detection,” in AAAI , 2022, pp. 3608–3616
2022
Later among the works it cites.
Z. Liu, Z. Zhang, Y. Tan, and W. Wu, “Boosting camouflaged object detection with dual-task interactive transformer,” in ICPR , 2022, pp. 140–146
2022
Later among the works it cites.
J. Ren, X. Hu, L. Zhu, X. Xu, Y. Xu, W. Wang, Z. Deng, and P.-A. Heng, “Deep texture-aware features for camouflaged object detection,” IEEE Transactions on Circuits and Systems for Video Technology , vol. 33, no. 3, pp. 1157–1167, 2023
2023
Closest in time.
T. Chen, J. Xiao, X. Hu, G. Zhang, and S. Wang, “Adaptive fusion network for rgb-d salient object detection,” Neurocomputing , vol. 522, pp. 152–164, 2023
2023
Closest in time.