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Donoser, M. Urschler, M. Hirzer, and H. Bischof, \u201cSaliency driven total variation segmentation,\u201d Int. Conf. Comput. Vis., pp.817-824, 2009. 10.1109\/iccv.2009.5459296","DOI":"10.1109\/ICCV.2009.5459296"},{"key":"2","unstructured":"[2] U. Rutishauser, D. Walther, C. Koch, and P. Perona, \u201cIs bottom up attention useful for object recognition?\u201d IEEE Conf. Comput. Vis. Pattern Recog., pp.II-II, 2004. 10.1109\/cvpr.2004.1315142"},{"key":"3","doi-asserted-by":"publisher","unstructured":"[3] Y. Wei, X. Liang, Y. Chen, X. Shen, M.-M. Cheng, J. Feng, Y. Zhao, and S. Yan, \u201cStc: A simple to complex framework for weakly-supervised semantic segmentation,\u201d IEEE Trans. Pattern Anal. Mach. Intell., vol.39, no.11, pp.2314-2320, 2017. 10.1109\/tpami.2016.2636150","DOI":"10.1109\/TPAMI.2016.2636150"},{"key":"4","doi-asserted-by":"crossref","unstructured":"[4] A. Borji, S. Frintrop, D.N. Sihite, and L. Itti, \u201cAdaptive object tracking by learning background context,\u201d IEEE Conf. Comput. Vis. Pattern Recog. Worksh., 2012. 10.1109\/cvprw.2012.6239191","DOI":"10.1109\/CVPRW.2012.6239191"},{"key":"5","doi-asserted-by":"publisher","unstructured":"[5] Y. Gao, M. Wang, D. Tao, R. Ji, and Q. Dai, \u201c3-D object retrieval and recognition with hypergraph analysis,\u201d IEEE Trans. Image Process., vol.21, no.9, pp.4290-4303, 2012. 10.1109\/tip.2012.2199502","DOI":"10.1109\/TIP.2012.2199502"},{"key":"6","doi-asserted-by":"publisher","unstructured":"[6] M.M. Cheng, Q.-B. Hou, S.-H. Zhang, and P.L. Rosin, \u201cIntelligent visual media processing: When graphics meets vision,\u201d J. Comput. Sci. Tech., vol.32, pp.110-121, 2017. 10.1007\/s11390-017-1681-7","DOI":"10.1007\/s11390-017-1681-7"},{"key":"7","doi-asserted-by":"crossref","unstructured":"[7] H. Jiang, J. Wang, Z. Yuan, T. Liu, N. Zheng, and S. Li, \u201cAutomatic salient object segmentation based on context and shape prior,\u201d Proc. British Machine Vision Conference, pp.110.1-110.12, 2011. 10.5244\/c.25.110","DOI":"10.5244\/C.25.110"},{"key":"8","doi-asserted-by":"crossref","unstructured":"[8] F. Perazzi, P. Kr\u00e4henb\u00fchl, Y. Pritch, and A. Hornung, \u201cSaliency filters: Contrast based filtering for salient region detection,\u201d Proc. IEEE Conference on Computer Vision and Pattern Recognition, pp.733-740, 2012. 10.1109\/cvpr.2012.6247743","DOI":"10.1109\/CVPR.2012.6247743"},{"key":"9","doi-asserted-by":"crossref","unstructured":"[9] C. Yang, L. Zhang, H. Lu, X. Ruan, and M.-H. Yang, \u201cSaliency detection via graph-based manifold ranking,\u201d Proc. IEEE Conference on Computer Vision and Pattern Recognition, pp.3166-3173, 2013. 10.1109\/cvpr.2013.407","DOI":"10.1109\/CVPR.2013.407"},{"key":"10","doi-asserted-by":"crossref","unstructured":"[10] Q. Yan, L. Xu, J. Shi, and J. Jia, \u201cHierarchical saliency detection,\u201d Proc. IEEE Conference on Computer Vision and Pattern Recognition, pp.1155-1162, 2013. 10.1109\/cvpr.2013.153","DOI":"10.1109\/CVPR.2013.153"},{"key":"11","doi-asserted-by":"crossref","unstructured":"[11] L. Itti and C. Koch, \u201cComputational modeling of visual attention,\u201d Nature reviews neuroscience, vol.2, no.3, pp.194-203, 2001. 10.1038\/35058500","DOI":"10.1038\/35058500"},{"key":"12","doi-asserted-by":"publisher","unstructured":"[12] D. Parkhurst, K. Law, and E. Niebur, \u201cModeling the role of salience in the allocation of overt visual attention,\u201d Vision research, vol.42, no.1, pp.107-123, 2002. 10.1016\/s0042-6989(01)00250-4","DOI":"10.1016\/S0042-6989(01)00250-4"},{"key":"13","doi-asserted-by":"crossref","unstructured":"[14] R. Achanta, S. Hemami, F. Estrada, and S. Susstrunk, \u201cFrequency tuned salient region detection,\u201d Proc. IEEE Conference on Computer Vision and Pattern Recognition, pp.1597-1604, 2009. 10.1109\/cvpr.2009.5206596","DOI":"10.1109\/CVPR.2009.5206596"},{"key":"14","doi-asserted-by":"crossref","unstructured":"[15] Y. Wei, F. Wen, W. Zhu, and J. Sun, \u201cGeodesic saliency using background priors,\u201d Proc. European Conference on Computer Vision, pp.29-42, 2012. 10.1007\/978-3-642-33712-3_3","DOI":"10.1007\/978-3-642-33712-3_3"},{"key":"15","unstructured":"[16] W.-C. Tu, S. He, Q. Yang, and S.-Y. Chien, \u201cReal-time salient object detection with a minimum spanning tree,\u201d Proc. IEEE Conference on Computer Vision and Pattern Recognition, pp.2334-2342, 2016. 10.1109\/cvpr.2016.256"},{"key":"16","unstructured":"[17] A. Krizhevsky, I. Sutskever, and G.E. Hinton, \u201cImagenet classification with deep convolutional neural networks,\u201d Advances in Neural Processing Systems, pp.1097-1105, 2012."},{"key":"17","unstructured":"[18] K. Simonyan and A. Zisserman, \u201cVery deep convolutional networks for large-scale image recognition,\u201d arXiv preprint arXiv:1409.1556, 2014."},{"key":"18","doi-asserted-by":"crossref","unstructured":"[19] R. Girshick, J. Donahue, T. Darrell, and J. Malik, \u201cRich feature hierarchies for accurate object detection and semantic segmentation,\u201d Proc. IEEE Conference on Computer Vision and Pattern Recognition, pp.580-587, 2014. 10.1109\/cvpr.2014.81","DOI":"10.1109\/CVPR.2014.81"},{"key":"19","doi-asserted-by":"publisher","unstructured":"[20] S. Ren, K. He, R. Girshick, and J. Sun, \u201cFaster R-CNN: Towards real-time object detection with region proposal networks,\u201d IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.39, no.6, pp.1137-1149, 2017. 10.1109\/tpami.2016.2577031","DOI":"10.1109\/TPAMI.2016.2577031"},{"key":"20","doi-asserted-by":"crossref","unstructured":"[21] L. Wang, W. Ouyang, X. Wang, and H. Lu, \u201cVisual tracking with fully convolutional networks,\u201d Proc. IEEE Conference on Computer Vision and Pattern Recognition, pp.3119-3127, 2015. 10.1109\/iccv.2015.357","DOI":"10.1109\/ICCV.2015.357"},{"key":"21","doi-asserted-by":"crossref","unstructured":"[22] L.J. Wang, W. Ouyang, X. Wang, and H. Lu, \u201cStct: Sequentially training convolutional networks for visual tracking,\u201d Proc. IEEE Conference on Computer Vision and Pattern Recognition, pp.1373-1381, 2016. 10.1109\/cvpr.2016.153","DOI":"10.1109\/CVPR.2016.153"},{"key":"22","doi-asserted-by":"crossref","unstructured":"[23] J. Long, E. Shelhamer, and T. Darrell, \u201cFully convolutional networks for semantic segmentation,\u201d Proc. IEEE Conference on Computer Vision and Pattern Recognition, pp.3431-3440, 2015. 10.1109\/cvpr.2015.7298965","DOI":"10.1109\/CVPR.2015.7298965"},{"key":"23","unstructured":"[24] L.C. Chen, G. Papandreou, I. Kokkinos, K. Murphy, and A.L. Yuille, \u201cSemantic image segmentation with deep convolutional nets and fully connected crfs,\u201d arXiv preprint arXiv: 1412.7062, 2014."},{"key":"24","doi-asserted-by":"crossref","unstructured":"[25] H. Noh, S. Hong, and B. Han, \u201cLearning deconvolution network for semantic segmentation,\u201d Proc. IEEE International Conference on Computer Vision, pp.1520-1528, 2015. 10.1109\/iccv.2015.178","DOI":"10.1109\/ICCV.2015.178"},{"key":"25","doi-asserted-by":"crossref","unstructured":"[26] S. Zheng, S. Jayasumana, B. Romera-Paredes, V. Vineet, Z. Su, D. Du, C. Huang, and P.H. Torr, \u201cConditional random fields as recurrent neural networks,\u201d Proc. IEEE International Conference on Computer Vision, pp.1529-1537, 2015. 10.1109\/iccv.2015.179","DOI":"10.1109\/ICCV.2015.179"},{"key":"26","unstructured":"[27] G. Li and Y. Yu, \u201cVisual saliency based on multiscale deep features,\u201d Proc. IEEE Conference on Computer Vision and Pattern Recognition, pp.5455-5463, 2015. 10.1109\/cvpr.2015.7299184"},{"key":"27","doi-asserted-by":"publisher","unstructured":"[28] X. Li, L. Zhao, L. Wei, M. Yang, F. Wu, Y. Zhuang, H. Ling, and J. Wang, \u201cDeepsaliency: Multi-task deep neural network model for salient object detection,\u201d IEEE Trans. Image Process., vol.25, no.8, pp.3919-3930, 2016. 10.1109\/tip.2016.2579306","DOI":"10.1109\/TIP.2016.2579306"},{"key":"28","doi-asserted-by":"crossref","unstructured":"[29] G. Lee, Y.-W. Tai, and J. Kim, \u201cDeep saliency with encoded low level distance map and high level features,\u201d Proc. IEEE Conference on Computer Vision and Pattern Recognition, pp.660-668, 2016. 10.1109\/cvpr.2016.78","DOI":"10.1109\/CVPR.2016.78"},{"key":"29","doi-asserted-by":"crossref","unstructured":"[30] L. Wang, H. Lu, X. Ruan, and M.-H. Yang, \u201cDeep networks for saliency detection via local estimation and global search,\u201d Proc. IEEE Conference on Computer Vision and Pattern Recognition, pp.3183-3192, 2015. 10.1109\/cvpr.2015.7298938","DOI":"10.1109\/CVPR.2015.7298938"},{"key":"30","doi-asserted-by":"crossref","unstructured":"[31] X. Wang, R. Girshick, A. Gupta, and K. He, \u201cNon-local neural networks,\u201d Proc. IEEE Conference on Computer Vision and Pattern Recognition, 2018. 10.1109\/cvpr.2018.00813","DOI":"10.1109\/CVPR.2018.00813"},{"key":"31","doi-asserted-by":"publisher","unstructured":"[32] L. Itti, C. Koch, and E. Niebur, \u201cA model of saliency-based visual attention for rapid scene analysis,\u201d IEEE Trans. Pattern Anal. Mach. Intell., vol.20, no.11, pp.1254-1259, 1998. 10.1109\/34.730558","DOI":"10.1109\/34.730558"},{"key":"32","doi-asserted-by":"crossref","unstructured":"[33] R. Valenti, N. Sebe, and T. Gevers, \u201cImage saliency by isocentric curvedness and color,\u201d ICCV, pp.2185-2192, 2009. 10.1109\/iccv.2009.5459240","DOI":"10.1109\/ICCV.2009.5459240"},{"key":"33","doi-asserted-by":"crossref","unstructured":"[34] D.A. Klein and S. Frintrop, \u201cCenter-surround divergence of feature statistics for salient object detection,\u201d ICCV, 2011. 10.1109\/iccv.2011.6126499","DOI":"10.1109\/ICCV.2011.6126499"},{"key":"34","doi-asserted-by":"crossref","unstructured":"[35] X. Li, Y. Li, C. Shen, A.R. Dick, and A. van den Hengel, \u201cContextual hypergraph modeling for salient object detection,\u201d ICCV, pp.3328-3335, 2013. 10.1109\/iccv.2013.413","DOI":"10.1109\/ICCV.2013.413"},{"key":"35","doi-asserted-by":"crossref","unstructured":"[36] M.M. Cheng, N.J. Mitra, X. Huang, P.H.S. Torr, and S.-M. Hu, \u201cGlobal contrast based salient region detection,\u201d IEEE Trans. Pattern Anal. Mach. Intell., vol.37, no.3, pp.569-582, 2014. 10.1109\/TPAMI.2014.2345401.2014","DOI":"10.1109\/TPAMI.2014.2345401"},{"key":"36","doi-asserted-by":"crossref","unstructured":"[37] M.-M. Cheng, J. Warrell, W.-Y. Lin, S. Zheng, V. Vineet, and N. Crook, \u201cEfficient salient region detection with soft image abstraction,\u201d ICCV, pp.1529-1536, 2013. 10.1109\/iccv.2013.193","DOI":"10.1109\/ICCV.2013.193"},{"key":"37","doi-asserted-by":"crossref","unstructured":"[38] Z. Jiang and L.S. Davis, \u201cSubmodular salient region detection,\u201d CVPR, pp.2043-2050, 2013. 10.1109\/cvpr.2013.266","DOI":"10.1109\/CVPR.2013.266"},{"key":"38","doi-asserted-by":"crossref","unstructured":"[39] X. Li, H. Lu, L. Zhang, X. Ruan, and M.-H. Yang, \u201cSaliency detection via dense and sparse reconstruction,\u201d ICCV, 2013. 10.1109\/iccv.2013.370","DOI":"10.1109\/ICCV.2013.370"},{"key":"39","doi-asserted-by":"crossref","unstructured":"[40] B. Jiang, L. Zhang, H. Lu, C. Yang, and M.-H. Yang, \u201cSaliency detection via absorbing markov chain,\u201d ICCV, 2013. 10.1109\/iccv.2013.209","DOI":"10.1109\/ICCV.2013.209"},{"key":"40","doi-asserted-by":"publisher","unstructured":"[41] S. He, R. Lau, W. Liu, Z. Huang, and Q. Yang, \u201cSupercnn: A superpixel-wise convolutional neural network for salient object detection,\u201d Int. J. Comput. Vis., vol.115, no.3, pp.330-344, 2015. 10.1007\/s11263-015-0822-0","DOI":"10.1007\/s11263-015-0822-0"},{"key":"41","doi-asserted-by":"crossref","unstructured":"[42] G. Lee, Y.-W. Tai, and J. Kim, \u201cDeep saliency with encoded low level distance map and high level features,\u201d Proc. IEEE Conference on Computer Vision and Pattern Recognition, pp.660-668, 2016. 10.1109\/cvpr.2016.78","DOI":"10.1109\/CVPR.2016.78"},{"key":"42","doi-asserted-by":"publisher","unstructured":"[43] Q. Hou, M. Cheng, X. Hu, A. Borji, Z. Tu, and P.H.S. Torr, \u201cDeeply supervised salient object detection with short connections,\u201d IEEE Trans. Pattern Anal. Mach. Intell., vol.41, no.4, pp.815-828, 2019. 10.1109\/tpami.2018.2815688","DOI":"10.1109\/TPAMI.2018.2815688"},{"key":"43","doi-asserted-by":"publisher","unstructured":"[44] L. Wang, L. Wang, H. Lu, P. Zhang, and X. Ruan, \u201cSalient object detection with recurrent fully convolutional networks,\u201d IEEE Trans. Pattern Anal. Mach. Intell., vol.41, no.7, pp.1734-1746, 2019. 10.1109\/tpami.2018.2846598","DOI":"10.1109\/TPAMI.2018.2846598"},{"key":"44","doi-asserted-by":"crossref","unstructured":"[45] G. Li and Y. Yu, \u201cDeep contrast learning for salient object detection,\u201d Proc. IEEE Conference on Computer Vision and Pattern Recognition, pp.478-487, 2016. 10.1109\/cvpr.2016.58","DOI":"10.1109\/CVPR.2016.58"},{"key":"45","doi-asserted-by":"crossref","unstructured":"[46] N. Liu and J.W. Han, \u201cDHSNet: Deep Hierarchical Saliency Network for Salient Object Detection,\u201d 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), IEEE, 2016. 10.1109\/cvpr.2016.80","DOI":"10.1109\/CVPR.2016.80"},{"key":"46","doi-asserted-by":"crossref","unstructured":"[47] Z. Luo, A, Mishra, A. Achkar, J. Eichel, S. Li, and P.-M. Jodoin, \u201cNon-local Deep Features for Salient Object Detection,\u201d 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), IEEE, 2017. 10.1109\/cvpr.2017.698","DOI":"10.1109\/CVPR.2017.698"},{"key":"47","doi-asserted-by":"crossref","unstructured":"[48] F. Yu, V. Koltun, and T. Funkhouser, \u201cDilated Residual Networks,\u201d 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), IEEE Computer Society, 2017. 10.1109\/cvpr.2017.75","DOI":"10.1109\/CVPR.2017.75"},{"key":"48","doi-asserted-by":"crossref","unstructured":"[49] K. He, X. Zhang, S. Ren, and J. Sun, \u201cDeep residual learning for image recognition,\u201d CVPR, pp.1, 2, 5, 2016. 10.1109\/cvpr.2016.90","DOI":"10.21476\/PP.2016.2179"},{"key":"49","unstructured":"[50] F. Yu and V. Koltun, \u201cMultiscale context aggregation by dilated convolutions,\u201d ICLR2016, arXiv: 1511.07122v3, 2016."},{"key":"50","doi-asserted-by":"publisher","unstructured":"[51] R. Achanta, A. Shaji, K. Smith, A. Lucchi, P. Fua, and S. S\u00fcsstrunk, \u201cSLIC superpixels compared to state-of-the-art superpixel methods,\u201d IEEE Trans. Pattern Anal. Mach. Intell., vol.34, no.11, pp.2274-2282, Nov. 2012. 10.1109\/tpami.2012.120","DOI":"10.1109\/TPAMI.2012.120"},{"key":"51","doi-asserted-by":"publisher","unstructured":"[52] T. Liu, Z. Yuan, J. Sun, J. Wang, N. Zheng, X. Tang, and H.-Y. Shum, \u201cLearning to detect a salient object,\u201d IEEE Trans. Pattern Anal. Mach. Intell., vol.33, no.2, pp.353-367, 2011. 10.1109\/tpami.2010.70","DOI":"10.1109\/TPAMI.2010.70"},{"key":"52","doi-asserted-by":"crossref","unstructured":"[53] Y. Li, X. Hou, C. Koch, J.M. Rehg, and A.L. Yuille, \u201cThe secrets of salient object segmentation,\u201d IEEE Conf. Comput. Vis. Pattern Recog., pp.280-287, 2014. 10.1109\/cvpr.2014.43","DOI":"10.1109\/CVPR.2014.43"},{"key":"53","doi-asserted-by":"crossref","unstructured":"[54] H. Jiang, J. Wang, Z. Yuan, Y. Wu, N. Zheng, and S. Li, \u201cSalient object detection: A discriminative regional feature integration approach,\u201d Proc. IEEE Conference on Computer Vision and Pattern Recognition, pp.2083-2090, 2013. 10.1109\/cvpr.2013.271","DOI":"10.1109\/CVPR.2013.271"},{"key":"54","doi-asserted-by":"crossref","unstructured":"[55] R. Margolin, A. Tal, and L. Zelnik-Manor, \u201cWhat makes a patch distinct?\u201d Proc. IEEE Conference on Computer Vision and Pattern Recognition, pp.1139-1146, 2013. 10.1109\/cvpr.2013.151","DOI":"10.1109\/CVPR.2013.151"},{"key":"55","unstructured":"[56] K.-Y. Chang, T.-L. Liu, H.-T. Chen, and S.-H. Lai, \u201cFusing generic objectness and visual saliency for salient object detection,\u201d International Conference on Computer Vision, IEEE Computer Society, 2011. 10.1109\/iccv.2011.6126333"},{"key":"56","doi-asserted-by":"crossref","unstructured":"[57] L. Wang, H. Lu, X. Ruan, and M.-H. Yang, \u201cDeep networks for saliency detection via local estimation and global search,\u201d Proc. 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