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Salient objects attract human attention and usually stand out clearly from their surroundings.
R. T. Hanlon and J. B. Messenger, “Adaptive coloration in young cuttlefish (sepia officinalis l.): the morphology and development of body patterns and their relation to behaviour,” Philosophical Transactions of the Royal Society of London. B, Biological Sciences
1988
Earlier work this paper cites.
R. R. Behrens, “The theories of Abbott H. Thayer: Father of camouflage,” Leonardo
1988
Earlier work this paper cites.
L. Itti, C. Koch, and E. Niebur, “A model of saliency-based visual attention for rapid scene analysis,” IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI)
1998
Earlier work this paper cites.
I. C. Cuthill, M. Stevens, J. Sheppard, T. Maddocks, C. A. Párraga, and T. S. Troscianko, “Disruptive coloration and background pattern matching,” Nature
2005
Earlier work this paper cites.
S. Chopra, R. Hadsell, and Y. LeCun, “Learning a similarity metric discriminatively, with application to face verification,” in IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
2005
Earlier work this paper cites.
L. N. Carvalho, J. Zuanon, and I. Sazima, “The almost invisible league: crypsis and association between minute fishes and shrimps as a possible defence against visually hunting predators,” Neotropical Ichthyology
2006
Earlier work this paper cites.
N. U. Bhajantri and P. Nagabhushan, “Camouflage defect identification: a novel approach,” in International Conference on Information Technology
2006
Earlier work this paper cites.
R. Hadsell, S. Chopra, and Y. LeCun, “Dimensionality reduction by learning an invariant mapping,” in IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
2006
Earlier work this paper cites.
R. Hanlon, “Cephalopod dynamic camouflage,” Current Biology
2007
Earlier work this paper cites.
N. Puzikova, E. Uvarova, I. Filyaev, and L. Yarovaya, “Principles of an approach for coloring military camouflage,” Fibre Chemistry
2008
Earlier work this paper cites.
M. Stevens and S. Merilaita, “Animal camouflage: current issues and new perspectives,” Philosophical Transactions of the Royal Society B: Biological Sciences
2009
Earlier work this paper cites.
K. Q. Weinberger and L. K. Saul, “Distance metric learning for large margin nearest neighbor classification.,” Journal of machine learning research
2009
Earlier work this paper cites.
G. Chechik, V. Sharma, U. Shalit, and S. Bengio, “Large scale online learning of image similarity through ranking,” Journal of Machine Learning Research
2010
Earlier work this paper cites.
M. Everingham, L. Van Gool, C. Williams, J. Winn, and A. Zisserman, “The pascal visual object classes (voc) challenge,” International Journal of Computer Vision (IJCV)
2010
Earlier work this paper cites.
V. Movahedi and J. H. Elder, “Design and perceptual validation of performance measures for salient object segmentation,” in IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Workshop
2010
Earlier work this paper cites.
Y. Wei, F. Wen, W. Zhu, and J. Sun, “Geodesic saliency using background priors,” in European Conference on Computer Vision (ECCV)
2012
Earlier work this paper cites.
C. Yang, L. Zhang, H. Lu, X. Ruan, and M.-H. Yang, “Saliency detection via graph-based manifold ranking,” in IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
2013
Earlier work this paper cites.
Q. Yan, L. Xu, J. Shi, and J. Jia, “Hierarchical saliency detection,” in IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
2013
Earlier work this paper cites.
D. P. Kingma and M. Welling, “Auto-encoding variational bayes,” in International Conference on Learning Representations (ICLR)
2014
Earlier work this paper cites.
I. Goodfellow, J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde-Farley, S. Ozair, A. Courville, and Y. Bengio, “Generative adversarial nets,” in Advances in Neural Information Processing Systems (NeurIPS)
2014
Earlier work this paper cites.
Y. Li, X. Hou, C. Koch, J. M. Rehg, and A. L. Yuille, “The secrets of salient object segmentation,” in IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
2014
Earlier work this paper cites.
K. Sohn, H. Lee, and X. Yan, “Learning structured output representation using deep conditional generative models,” in Advances in Neural Information Processing Systems (NeurIPS)
2015
Earlier work this paper cites.
F. Schroff, D. Kalenichenko, and J. Philbin, “Facenet: A unified embedding for face recognition and clustering,” in IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
2015
Earlier work this paper cites.
O. Ronneberger, P. Fischer, and T. Brox, “U-net: Convolutional networks for biomedical image segmentation,” in Medical Image Computing and Computer-Assisted Intervention
2015
Earlier work this paper cites.
G. Li and Y. Yu, “Visual saliency based on multiscale deep features,” in IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
2015
Earlier work this paper cites.
S. S. S. Kruthiventi, V. Gudisa, J. H. Dholakiya, and R. V. Babu, “Saliency unified: A deep architecture for simultaneous eye fixation prediction and salient object segmentation,” in IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
2016
Earlier work this paper cites.
Y. Gal and Z. Ghahramani, “Dropout as a bayesian approximation: Representing model uncertainty in deep learning,” in International Conference on Machine Learning (ICML)
2016
Earlier work this paper cites.
I. Osband, C. Blundell, A. Pritzel, and B. Van Roy, “Deep exploration via bootstrapped dqn,” in Advances in Neural Information Processing Systems (NeurIPS)
2016
Earlier work this paper cites.
K. Sohn, “Improved deep metric learning with multi-class n-pair loss objective,” in Advances in Neural Information Processing Systems (NeurIPS)
2016
Earlier work this paper cites.
K. He, X. Zhang, S. Ren, and J. Sun, “Deep residual learning for image recognition,” in IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
2016
Earlier work this paper cites.
S.-C. Chen, B.-S. Huang, C.-Y. Lin, K.-H. Fan, J. T.-C. Chang, S.-C. Wu, and Y.-H. Lai, “Psychosocial effects of a skin camouflage program in female survivors with head and neck cancer: a randomized controlled trial,” Psycho-oncology
2017
Earlier work this paper cites.
V. Kalogeiton, P. Weinzaepfel, V. Ferrari, and C. Schmid, “Joint learning of object and action detectors,” in IEEE International Conference on Computer Vision (ICCV)
2017
Earlier work this paper cites.
T.-Y. Lin, P. Goyal, R. Girshick, K. He, and P. Dollár, “Focal loss for dense object detection,” in IEEE International Conference on Computer Vision (ICCV)
2017
Earlier work this paper cites.
X. Li, Z. Liu, P. Luo, C. Change Loy, and X. Tang, “Not all pixels are equal: Difficulty-aware semantic segmentation via deep layer cascade,” in IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
2017
Earlier work this paper cites.
B. Lakshminarayanan, A. Pritzel, and C. Blundell, “Simple and scalable predictive uncertainty estimation using deep ensembles,” in Advances in Neural Information Processing Systems (NeurIPS)
2017
Earlier work this paper cites.
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 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
2017
Earlier work this paper cites.
2017
Earlier work this paper cites.
A. Kendall and Y. Gal, “What uncertainties do we need in bayesian deep learning for computer vision?,” in Advances in Neural Information Processing Systems (NeurIPS)
2017
Earlier work this paper cites.
Z. Luo, A. Mishra, A. Achkar, J. Eichel, S. Li, and P.-M. Jodoin, “Non-local deep features for salient object detection,” in IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
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 IEEE International Conference on Computer Vision (ICCV)
2017
Cited alongside, same era.
W. Wang, J. Shen, X. Dong, and A. Borji, “Salient object detection driven by fixation prediction,” in IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
2018
Cited alongside, same era.
R. R. Behrens, “Seeing through camouflage: Abbott Thayer, background-picturing and the use of cutout silhouettes,” Leonardo
2018
Cited alongside, same era.
T. W. Pike, “Quantifying camouflage and conspicuousness using visual salience,” Methods in Ecology and Evolution
2018
Cited alongside, same era.
X. Li, L. Yu, Y. Jin, C.-W. Fu, L. Xing, and P.-A. Heng, “Difficulty-aware meta-learning for rare disease diagnosis,” in Medical Image Computing and Computer Assisted Intervention
2020
Later among the works it cites.
S. Yu, H.-Y. Zhou, K. Ma, C. Bian, C. Chu, H. Liu, and Y. Zheng, “Difficulty-aware glaucoma classification with multi-rater consensus modeling,” in Medical Image Computing and Computer Assisted Intervention
2020
Later among the works it cites.
S. Xie, Z. Feng, Y. Chen, S. Sun, C. Ma, and M. Song, “Deal: Difficulty-aware active learning for semantic segmentation,” in Asian Conference on Computer Vision (ACCV)
2020
Later among the works it cites.
2020
Later among the works it cites.
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2018
Cited alongside, same era.
Y. Zhang, K. Li, K. Li, L. Wang, B. Zhong, and Y. Fu, “Image super-resolution using very deep residual channel attention networks,” in European Conference on Computer Vision (ECCV)
2018
Cited alongside, same era.
2018
Cited alongside, same era.
2018
Cited alongside, same era.
2018
Cited alongside, same era.
P. Skurowski, H. Abdulameer, J. Baszczyk, T. Depta, A. Kornacki, and P. Kozie, “Animal camouflage analysis: Chameleon database.,” Unpublished manuscript
2018
Cited alongside, same era.
2018
Cited alongside, same era.
B. Han, Q. Yao, X. Yu, G. Niu, M. Xu, W. Hu, I. Tsang, and M. Sugiyama, “Co-teaching: Robust training of deep neural networks with extremely noisy labels,” Advances in neural information processing systems
2018
Cited alongside, same era.
P. O. O Pinheiro, A. Almahairi, R. Benmalek, F. Golemo, and A. C. Courville, “Unsupervised learning of dense visual representations,” Advances in Neural Information Processing Systems (NeurIPS)
2020
Later among the works it cites.
K. Chaitanya, E. Erdil, N. Karani, and E. Konukoglu, “Contrastive learning of global and local features for medical image segmentation with limited annotations,” Advances in Neural Information Processing Systems (NeurIPS)
2020
Later among the works it cites.
P. Khosla, P. Teterwak, C. Wang, A. Sarna, Y. Tian, P. Isola, A. Maschinot, C. Liu, and D. Krishnan, “Supervised contrastive learning,” in Advances in Neural Information Processing Systems (NeurIPS)
2020
Later among the works it cites.
R. Ranftl, K. Lasinger, D. Hafner, K. Schindler, and V. Koltun, “Towards robust monocular depth estimation: Mixing datasets for zero-shot cross-dataset transfer,” IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI)
2020
Later among the works it cites.
S.-H. Gao, Y.-Q. Tan, M.-M. Cheng, C. Lu, Y. Chen, and S. Yan, “Highly efficient salient object detection with 100k parameters,” in European Conference on Computer Vision (ECCV)
2020
Later among the works it cites.
J. Wei, S. Wang, Z. Wu, C. Su, Q. Huang, and Q. Tian, “Label decoupling framework for salient object detection,” in IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
2020
Later among the works it cites.
X. Zhao, Y. Pang, L. Zhang, H. Lu, and L. Zhang, “Suppress and balance: A simple gated network for salient object detection,” in European Conference on Computer Vision (ECCV)
2020
Later among the works it cites.
A. Li, J. Zhang, Y. Lyu, B. Liu, T. Zhang, and Y. Dai, “Uncertainty-aware joint salient object and camouflaged object detection,” in IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
2021
Later among the works it cites.
H. Mei, G.-P. Ji, Z. Wei, X. Yang, X. Wei, and D.-P. Fan, “Camouflaged object segmentation with distraction mining,” in IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
2021
Later among the works it cites.
Q. Zhai, X. Li, F. Yang, C. Chen, H. Cheng, and D.-P. Fan, “Mutual graph learning for camouflaged object detection,” in IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
2021
Later among the works it cites.
Y. Lv, J. Zhang, Y. Dai, A. Li, B. Liu, N. Barnes, and D.-P. Fan, “Simultaneously localize, segment and rank the camouflaged objects,” in IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
2021
Later among the works it cites.
J. Zhang, D.-P. Fan, Y. Dai, S. Anwar, F. Saleh, S. Aliakbarian, and N. Barnes, “Uncertainty inspired rgb-d saliency detection,” IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI)
2021
Later among the works it cites.
P. Zhang, W. Liu, Y. Zeng, Y. Lei, and H. Lu, “Looking for the detail and context devils: High-resolution salient object detection,” IEEE Transactions on Image Processing (TIP)
2021
Later among the works it cites.
Y. Liu, X.-Y. Zhang, J.-W. Bian, L. Zhang, and M.-M. Cheng, “Samnet: Stereoscopically attentive multi-scale network for lightweight salient object detection,” IEEE Transactions on Image Processing (TIP)
2021
Later among the works it cites.
Y. Huang, S. Ahmad, J. Fan, D. Shen, and P.-T. Yap, “Difficulty-aware hierarchical convolutional neural networks for deformable registration of brain mr images,” Medical image analysis
2021
Later among the works it cites.
Z. Xie, Y. Lin, Z. Zhang, Y. Cao, S. Lin, and H. Hu, “Propagate yourself: Exploring pixel-level consistency for unsupervised visual representation learning,” in IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
2021
Later among the works it cites.
X. Li, Y. Zhou, Y. Zhang, A. Zhang, W. Wang, N. Jiang, H. Wu, and W. Wang, “Dense semantic contrast for self-supervised visual representation learning,” in ACM International Conference on Multimedia (MM)
2021
Later among the works it cites.
W. Van Gansbeke, S. Vandenhende, S. Georgoulis, and L. Van Gool, “Unsupervised semantic segmentation by contrasting object mask proposals,” in IEEE International Conference on Computer Vision (ICCV)
2021
Later among the works it cites.
X. Wang, R. Zhang, C. Shen, T. Kong, and L. Li, “Dense contrastive learning for self-supervised visual pre-training,” in IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
2021
Later among the works it cites.
E. Xie, J. Ding, W. Wang, X. Zhan, H. Xu, P. Sun, Z. Li, and P. Luo, “Detco: Unsupervised contrastive learning for object detection,” in IEEE International Conference on Computer Vision (ICCV)
2021
Later among the works it cites.
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 AAAI Conference on Artificial Intelligence (AAAI)
2021
Later among the works it cites.
M. Zhang, T. Liu, Y. Piao, S. Yao, and H. Lu, “Auto-msfnet: Search multi-scale fusion network for salient object detection,” in ACM International Conference on Multimedia (MM)
2021
Later among the works it cites.
S.-H. Gao, M.-M. Cheng, K. Zhao, X.-Y. Zhang, M.-H. Yang, and P. Torr, “Res2net: A new multi-scale backbone architecture,” IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI)
2021
Later among the works it cites.
D.-P. Fan, G.-P. Ji, M.-M. Cheng, and L. Shao, “Concealed object detection,” IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI)
2021
Later among the works it cites.
Y. Sun, G. Chen, T. Zhou, Y. Zhang, and N. Liu, “Context-aware cross-level fusion network for camouflaged object detection,” in International Joint Conference on Artificial Intelligence (IJCAI)
2021
Later among the works it cites.
Y. Pang, X. Zhao, T.-Z. Xiang, L. Zhang, and H. Lu, “Zoom in and out: A mixed-scale triplet network for camouflaged object detection,” in IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
2022
Later among the works it cites.
T. Zhang, C. Qiu, W. Ke, S. Süsstrunk, and M. Salzmann, “Leverage your local and global representations: A new self-supervised learning strategy,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition
2022
Later among the works it cites.
J. Xie, J. Xiang, J. Chen, X. Hou, X. Zhao, and L. Shen, “C2am: contrastive learning of class-agnostic activation map for weakly supervised object localization and semantic segmentation,” in IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
2022
Later among the works it cites.
Z. Wu, S. Li, C. Chen, A. Hao, and H. Qin, “Recursive multi-model complementary deep fusion for robust salient object detection via parallel sub-networks,” Pattern Recognition (PR)
2022
Later among the works it cites.
Y.-H. Wu, Y. Liu, L. Zhang, M.-M. Cheng, and B. Ren, “Edn: Salient object detection via extremely-downsampled network,” IEEE Transactions on Image Processing (TIP)
2022
Later among the works it cites.
Z. Yang, S. Soltanian-Zadeh, and S. Farsiu, “Biconnet: an edge-preserved connectivity-based approach for salient object detection,” Pattern Recognition (PR)
2022
Later among the works it cites.
G.-P. Ji, L. Zhu, M. Zhuge, and K. Fu, “Fast camouflaged object detection via edge-based reversible re-calibration network,” Pattern Recognition (PR)
2022
Later among the works it cites.
G. Franchi, X. Yu, A. Bursuc, E. Aldea, S. Dubuisson, and D. Filliat, “Latent discriminant deterministic uncertainty,” in European Conference on Computer Vision (ECCV)
2022
Later among the works it cites.
Y. Lv, J. Zhang, Y. Dai, A. Li, N. Barnes, and D.-P. Fan, “Towards deeper understanding of camouflaged object detection,” IEEE Transactions on Circuits and Systems for Video Technology (TCSVT)
2023
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