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Camouflaged object detection (COD), which aims to identify the objects that conceal themselves into the surroundings, has recently drawn increasing research efforts in the field of computer vision.
G. H. Thayer, Concealing-coloration in the animal kingdom: an exposition of the laws of disguise through color and pattern: being a summary of Abbott H. Thayer’s discoveries . Macmillan Company, 1918
1918
Earlier work this paper cites.
A. C. Copeland and M. M. Trivedi, “Signature strength metrics for camouflaged targets corresponding to human perceptual cues,” Optical Engineering , vol. 37, 1998
1998
Earlier work this paper cites.
S. Goferman, L. Zelnik-Manor, and A. Tal, “Context-aware saliency detection,” IEEE transactions on pattern analysis and machine intelligence , vol. 34, no. 10, pp. 1915–1926, 2011
2011
Earlier work this paper cites.
X. Glorot, A. Bordes, and Y. Bengio, “Deep sparse rectifier neural networks,” in Proceedings of the fourteenth international conference on artificial intelligence and statistics , 2011, pp. 315–323
2011
Earlier work this paper cites.
A. Owens, C. Barnes, A. Flint, H. Singh, and W. Freeman, “Camouflaging an object from many viewpoints,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition , 2014, pp. 2782–2789
2014
Earlier work this paper cites.
K. He, X. Zhang, S. Ren, and J. Sun, “Spatial pyramid pooling in deep convolutional networks for visual recognition,” IEEE Transactions on Pattern Analysis and Machine Intelligence , vol. 37, no. 9, pp. 1904–16, 2014
2014
Earlier work this paper cites.
R. Margolin, L. Zelnik-Manor, and A. Tal, “How to evaluate foreground maps?” in Proceedings of the IEEE conference on computer vision and pattern recognition , 2014, pp. 248–255
2014
Earlier work this paper cites.
J. Silva, A. Histace, O. Romain, X. Dray, and B. Granado, “Toward embedded detection of polyps in wce images for early diagnosis of colorectal cancer,” International Journal of Computer Assisted Radiology and Surgery , vol. 9, no. 2, pp. 283–293, 2014
2014
Earlier work this paper cites.
J. Long, E. Shelhamer, and T. Darrell, “Fully convolutional networks for semantic segmentation,” in Proceedings of the IEEE conference on computer vision and pattern recognition , 2015, pp. 3431–3440
2015
Earlier work this paper cites.
C. Szegedy, W. Liu, Y. Jia, P. Sermanet, S. Reed, D. Anguelov, D. Erhan, V. Vanhoucke, and A. Rabinovich, “Going deeper with convolutions,” in Proceedings of the IEEE conference on computer vision and pattern recognition , 2015, pp. 1–9
2015
Earlier work this paper cites.
2015
Earlier work this paper cites.
X. Zhang, C. Zhu, S. Wang, Y. Liu, and M. Ye, “A bayesian approach to camouflaged moving object detection,” IEEE transactions on circuits and systems for video technology , vol. 27, no. 9, pp. 2001–2013, 2016
2016
Earlier work this paper cites.
F. Milletari, N. Navab, and S.-A. Ahmadi, “V-net: Fully convolutional neural networks for volumetric medical image segmentation,” in 3DV , 2016
2016
Earlier work this paper cites.
L. Chen, H. Zhang, J. Xiao, L. Nie, J. Shao, W. Liu, and T.-S. Chua, “Sca-cnn: Spatial and channel-wise attention in convolutional networks for image captioning,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition , 2017, pp. 5659–5667
2017
Earlier work this paper cites.
L.-C. Chen, G. Papandreou, I. Kokkinos, K. Murphy, and A. L. Yuille, “Deeplab: Semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected crfs,” IEEE transactions on pattern analysis and machine intelligence , vol. 40, no. 4, pp. 834–848, 2017
2017
Earlier work this paper cites.
C. Peng, X. Zhang, G. Yu, G. Luo, and J. Sun, “Large kernel matters–improve semantic segmentation by global convolutional network,” in Proceedings of the IEEE conference on computer vision and pattern recognition , 2017, pp. 4353–4361
2017
Earlier work this paper cites.
D.-P. Fan, M.-M. Cheng, Y. Liu, T. Li, and A. Borji, “Structure-measure: A new way to evaluate foreground maps,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition , 2017, pp. 4548–4557
2017
Earlier work this paper cites.
2017
Cited alongside, same era.
X. Wang, R. Girshick, A. Gupta, and K. He, “Non-local neural networks,” in Proceedings of the IEEE conference on computer vision and pattern recognition , 2018, pp. 7794–7803
2018
Cited alongside, same era.
J. Hu, L. Shen, and G. Sun, “Squeeze-and-excitation networks,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition , 2018, pp. 7132–7141
2018
Cited alongside, same era.
2018
Cited alongside, same era.
Z. Yao and L. Wang, “Boundary information progressive guidance network for salient object detection,” IEEE Transactions on Multimedia , vol. 24, pp. 4236–4249, 2021
2021
Closest in time.
Q. Zhai, X. Li, F. Yang, C. Chen, H. Cheng, and D.-P. Fan, “Mutual graph learning for camouflaged object detection,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2021
2021
Closest in time.
H. Mei, G.-P. Ji, Z. Wei, X. Yang, X. Wei, and D.-P. Fan, “Camouflaged object segmentation with distraction mining,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) , June 2021, pp. 8772–8781
2021
Closest in time.
F. Yang, Q. Zhai, X. Li, R. Huang, H. Cheng, and D.-P. Fan, “Uncertainty-guided transformer reasoning for camouflaged object detection,” in IEEE International Conference on Computer Vision(ICCV) , 2021
2021
Closest in time.
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2018
Cited alongside, same era.
S. Liu, D. Huang et al. , “Receptive field block net for accurate and fast object detection,” in Proceedings of the European Conference on Computer Vision (ECCV) , 2018, pp. 385–400
2018
Cited alongside, same era.
Z. Zhou, M. M. Rahman Siddiquee, N. Tajbakhsh, and J. Liang, “Unet++: A nested u-net architecture for medical image segmentation,” in DLMIA , 2018, pp. 3–11
2018
Cited alongside, same era.
D.-P. Fan, C. Gong, Y. Cao, B. Ren, M.-M. Cheng, and A. Borji, “Enhanced-alignment measure for binary foreground map evaluation,” International Joint Conferences on Artificial Intelligence , 2018
2018
Cited alongside, same era.
T.-N. Le, T. V. Nguyen, Z. Nie, M.-T. Tran, and A. Sugimoto, “Anabranch network for camouflaged object segmentation,” Computer Vision and Image Understanding , vol. 184, pp. 45–56, 2019
2019
Cited alongside, same era.
J. Fu, J. Liu, H. Tian, Y. Li, Y. Bao, Z. Fang, and H. Lu, “Dual attention network for scene segmentation,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition , 2019, pp. 3146–3154
2019
Cited alongside, same era.
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 Conference on Computer Vision and Pattern Recognition , 2019, pp. 8779–8788
2019
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
Cited alongside, same era.
Y. Lyu, J. Zhang, Y. Dai, A. Li, B. Liu, N. Barnes, and D.-P. Fan, “Simultaneously localize, segment and rank the camouflaged objects,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) , 2021
2021
Closest in time.
A. Li, J. Zhang, Y. Lyu, B. Liu, T. Zhang, and Y. Dai, “Uncertainty-aware joint salient object and camouflaged object detection,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) , 2021
2021
Closest in time.
M.-M. Chen and D.-P. Fan, “Structure-measure: A new way to evaluate foreground maps,” International Journal of Computer Vision , vol. 129, pp. 2622–2638, 2021
2021
Closest in time.
T.-N. Le, Y. Cao, T.-C. Nguyen, M.-Q. Le, K.-D. Nguyen, T.-T. Do, M.-T. Tran, and T. V. Nguyen, “Camouflaged instance segmentation in-the-wild: Dataset, method, and benchmark suite,” IEEE Transactions on Image Processing , vol. 31, pp. 287–300, 2021
2021
Closest in time.
D.-P. Fan, G.-P. Ji, M.-M. Cheng, and L. Shao, “Concealed object detection,” IEEE Transactions on Pattern Analysis and Machine Intelligence , 2022
2022
Closest in time.
W. Zhai, Y. Cao, H. Xie, and Z.-J. Zha, “Deep texton-coherence network for camouflaged object detection,” IEEE Transactions on Multimedia , vol. 25, pp. 5155–5165, 2022
2022
Closest in time.
G. Chen, S.-J. Liu, Y.-J. Sun, G.-P. Ji, Y.-F. Wu, and T. Zhou, “Camouflaged object detection via context-aware cross-level fusion,” IEEE Transactions on Circuits and Systems for Video Technology , vol. 32, no. 10, pp. 6981–6993, 2022
2022
Closest in time.
G.-P. Ji, L. Zhu, M. Zhuge, and K. Fu, “Fast camouflaged object detection via edge-based reversible re-calibration network,” Pattern Recognition , vol. 123, p. 108414, 2022
2022
Closest in time.
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 Proceedings of the AAAI conference on artificial intelligence , vol. 36, no. 3, 2022, pp. 3608–3616
2022
Closest in time.
Y. Lyu, H. Zhang, Y. Li, H. Liu, Y. Yang, and D. Yuan, “Uedg: uncertainty-edge dual guided camouflage object detection,” IEEE Transactions on Multimedia , 2023
2023
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T. Wang, J. Wang, and R. Wang, “Camouflaged object detection with a feature lateral connection network,” Electronics , vol. 12, no. 12, 2023
2023
Closest in time.
X. Zhou, Z. Wu, and R. Cong, “Decoupling and integration network for camouflaged object detection,” IEEE Transactions on Multimedia , 2024
2024
Closest in time.