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Top-down saliency models produce a probability map that peaks at target locations specified by a task/goal such as object detection.
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A. Opelt, A. Pinz, M. Fussenegger, and P. Auer, “Generic object recognition with boosting,” PAMI , vol. 28, no. 3, pp. 416–431, 2006
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M. Everingham, L. Van Gool, C. K. I. Williams, J. Winn, and A. Zisserman, “The PASCAL Visual Object Classes Challenge 2007 (VOC2007) Results,” http://www.pascal-network.org/challenges/VOC/voc2007/workshop/index.html
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2010
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J. Wang, J. Yang, K. Yu, F. Lv, T. Huang, and Y. Gong, “Locality-constrained linear coding for image classification,” in CVPR , 2010
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M. Everingham, L. Van Gool, C. K. I. Williams, J. Winn, and A. Zisserman, “The PASCAL Visual Object Classes Challenge 2010 (VOC2010) Results,” http://www.pascal-network.org/challenges/VOC/voc2010/workshop/index.html
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D. Aldavert, A. Ramisa, R. L. de Mantaras, and R. Toledo, “Fast and robust object segmentation with the integral linear classifier,” in CVPR , 2010
2010
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A. Joulin, F. Bach, and J. Ponce, “Discriminative clustering for image co-segmentation,” in CVPR , 2010
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T. Liu, Z. Yuan, J. Sun, J. Wang, N. Zheng, X. Tang, and H.-Y. Shum, “Learning to detect a salient object,” PAMI , vol. 33, no. 2, pp. 353–367, 2011
2011
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B. Yao, A. Khosla, and L. Fei-Fei, “Combining randomization and discrimination for fine-grained image categorization,” in CVPR , 2011
2011
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G. Kim, E. P. Xing, L. Fei-Fei, and T. Kanade, “Distributed cosegmentation via submodular optimization on anisotropic diffusion,” in ICCV , 2011
2011
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G. Sharma, F. Jurie, and C. Schmid, “Discriminative spatial saliency for image classification,” in CVPR , 2012, pp. 3506–3513
2012
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J. Yang and M.-H. Yang, “Top-down visual saliency via joint crf and dictionary learning,” in CVPR , 2012
2012
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2012
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M. Everingham, L. Van Gool, C. K. I. Williams, J. Winn, and A. Zisserman, “The pascal visual object classes challenge 2012 (voc2012) results,” http://www.pascal-network.org/challenges/VOC/voc2012/workshop/index.html
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M. Marcin and S. Cordelia, “Accurate object recognition with shape masks,” Int. J. Comput. Vision , vol. 97, no. 2, pp. 191–209, 2012
2012
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J. Armand, B. Francis, and P. Jean, “Multi-class cosegmentation,” in CVPR , 2012
2012
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Y. Jia and M. Han, “Category-independent object-level saliency detection,” in ICCV , 2013
2013
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N. Khan and M. F. Tappen, “Discriminative dictionary learning with spatial priors.” in ICIP , 2013
2013
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J. R. Uijlings, K. E. van de Sande, T. Gevers, and A. W. Smeulders, “Selective search for object recognition,” Int. J. Comput. Vision , vol. 104, no. 2, pp. 154–171, 2013
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S. Shalev-Shwartz and T. Zhang, “Stochastic dual coordinate ascent methods for regularized loss minimization,” J. Mach. Learn. Res. , vol. 14, no. Feb, pp. 567–599, 2013
2013
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M. Rubinstein, A. Joulin, J. Kopf, and C. Liu, “Unsupervised joint object discovery and segmentation in internet images,” in CVPR , 2013
2013
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C. Yang, L. Zhang, H. Lu, X. Ruan, and M.-H. Yang, “Saliency detection via graph-based manifold ranking,” in CVPR , 2013, pp. 3166–3173
2013
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R. Girshick, J. Donahue, T. Darrell, and J. Malik, “Rich feature hierarchies for accurate object detection and semantic segmentation,” in CVPR , 2014, pp. 580–587
2014
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A. Kocak, K. Cizmeciler, A. Erdem, and E. Erdem, “Top down saliency estimation via superpixel-based discriminative dictionaries,” in BMVC , 2014
2014
Cited alongside, same era.
2014
Cited alongside, same era.
K. Simonyan, A. Vedaldi, and A. Zisserman, “Deep inside convolutional networks: Visualising image classification models and saliency maps,” in ICLR Workshop , 2014
2014
Cited alongside, same era.
J. Zhu, Y. Qiu, R. Zhang, J. Huang, and W. Zhang, “Top-down saliency detection via contextual pooling,” J. Signal Process. Syst. , vol. 74, no. 1, pp. 33–46, 2014
P. O. Pinheiro and R. Collobert, “From image-level to pixel-level labeling with convolutional networks,” June 2015
2015
Later among the works it cites.
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2015
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R. Girshick, “Fast r-cnn,” in ICCV , 2015, pp. 1440–1448
2015
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S. Ren, K. He, R. Girshick, and J. Sun, “Faster r-cnn: Towards real-time object detection with region proposal networks,” in NIPS. , 2015, pp. 91–99
2015
Later among the works it cites.
P. Jiang, N. Vasconcelos, and J. Peng, “Generic promotion of diffusion-based salient object detection,” in ICCV , 2015, pp. 217–225
2015
Later among the works it cites.
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2014
Cited alongside, same era.
Y. Li, X. Hou, C. Koch, J. M. Rehg, and A. L. Yuille, “The secrets of salient object segmentation,” in CVPR , 2014
2014
Cited alongside, same era.
2014
Cited alongside, same era.
M. Oquab, L. Bottou, I. Laptev, and J. Sivic, “Learning and transferring mid-level image representations using convolutional neural networks,” in CVPR , 2014, pp. 1717–1724
2014
Cited alongside, same era.
C. L. Zitnick and P. Dollár, “Edge boxes: Locating object proposals from edges,” in ECCV , 2014, pp. 391–405
2014
Cited alongside, same era.
M. D. Zeiler and R. Fergus, “Visualizing and understanding convolutional networks,” in ECCV , 2014, pp. 818–833
2014
Cited alongside, same era.
2014
Cited alongside, same era.
2014
Cited alongside, same era.
Ç. Aytekin, E. C. Ozan, S. Kiranyaz, and M. Gabbouj, “Visual saliency by extended quantum cuts,” in ICIP , 2015, pp. 1692–1696
2015
Later among the works it cites.
W.-C. Tu, S. He, Q. Yang, and S.-Y. Chien, “Real-time salient object detection with a minimum spanning tree,” in CVPR , June 2016
2016
Closest in time.
J. Yang and M.-H. Yang, “Top-down visual saliency via joint crf and dictionary learning,” PAMI , 2016
2016
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S. He, R. W. Lau, and Q. Yang, “Exemplar-driven top-down saliency detection via deep association,” in CVPR , 2016
2016
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H. Cholakkal, J. Johnson, and D. Rajan, “Backtracking scspm image classifier for weakly supervised top-down saliency,” in CVPR , 2016
2016
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J. Zhang, Z. Lin, S. X. Brandt, Jonathan, and S. Sclaroff, “Top-down neural attention by excitation backprop,” in ECCV , 2016
2016
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H. Cholakkal, J. Johnson, and D. Rajan, “A classifier-guided approach for top-down salient object detection,” Signal Process. Image Commun. , vol. 45, pp. 24–40, 2016
2016
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N. Liu and J. Han, “Dhsnet: Deep hierarchical saliency network for salient object detection,” in CVPR , June 2016
2016
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A. Mahendran and A. Vedaldi, “Salient deconvolutional networks,” in ECCV , 2016
2016
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W. Shimoda and K. Yanai, “Distinct class-specific saliency maps for weakly supervised semantic segmentation,” in ECCV , 2016
2016
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R. G. Cinbis, J. Verbeek, and C. Schmid, “Weakly supervised object localization with multi-fold multiple instance learning,” PAMI , 2016
2016
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Z. Bolei, K. Aditya, L. Agata, O. Aude, and T. Antonio, “Learning deep features for discriminative localization,” in CVPR , 2016
2016
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H. Bilen and A. Vedaldi, “Weakly supervised deep detection networks,” in CVPR , 2016
2016
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C. Sun, M. Paluri, R. Collobert, R. Nevatia, and L. Bourdev, “Pronet: Learning to propose object-specific boxes for cascaded neural networks,” in CVPR , 2016
2016
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R. Quan, J. Han, D. Zhang, and F. Nie, “Object co-segmentation via graph optimized-flexible manifold ranking,” in CVPR , June 2016
2016
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D. Zhang, J. Han, C. Li, J. Wang, and X. Li, “Detection of co-salient objects by looking deep and wide,” Int. J. Comput. Vision , pp. 1–18, 2016
2016
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X. Qi, Z. Liu, J. Shi, H. Zhao, and J. Jia, “Augmented feedback in semantic segmentation under image level supervision,” in ECCV , 2016, pp. 90–105
2016
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A. Kolesnikov and C. H. Lampert, “Seed, expand and constrain: Three principles for weakly-supervised image segmentation,” in ECCV , 2016, pp. 695–711
2016
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F. Saleh, M. S. A. Akbarian, M. Salzmann, L. Petersson, S. Gould, and J. M. Alvarez, “Built-in foreground/background prior for weakly-supervised semantic segmentation,” in ECCV . Springer, 2016, pp. 413–432
2016
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Y. Wei, X. Liang, Y. Chen, X. Shen, M.-M. Cheng, J. Feng, Y. Zhao, and S. Yan, “Stc: A simple to complex framework for weakly-supervised semantic segmentation,” PAMI , 2016
2016
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K. R. Jerripothula, J. Cai, and J. Yuan, “Object detection with discriminatively trained part-based models,” TMM , 2016
2016
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A. J. Bency, H. Kwon, H. Lee, S. Karthikeyan, and B. S. Manjunath, “Weakly supervised localization using deep feature maps,” in ECCV , 2016
2016
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J. Redmon, S. Divvala, R. Girshick, and A. Farhadi, “You only look once: Unified, real-time object detection,” in CVPR , 2016
2016
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D. Li, J. B. Huang, Y. Li, S. Wang, and M. H. Yang, “Weakly supervised object localization with progressive domain adaptation,” in CVPR , June 2016, pp. 3512–3520
2016
Closest in time.