{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,17]],"date-time":"2026-03-17T18:22:55Z","timestamp":1773771775567,"version":"3.50.1"},"reference-count":133,"publisher":"Association for Computing Machinery (ACM)","issue":"4","license":[{"start":{"date-parts":[[2018,1,30]],"date-time":"2018-01-30T00:00:00Z","timestamp":1517270400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"funder":[{"name":"Excellent Doctorate Foundation through Northwestern Polytechnical University"},{"DOI":"10.13039\/501100001809","name":"National Science Foundation of China","doi-asserted-by":"crossref","award":["61522207 and 61473231"],"award-info":[{"award-number":["61522207 and 61473231"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"crossref"}]},{"name":"Doctorate Foundation through Northwestern Polytechnical University"}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["ACM Trans. Intell. Syst. Technol."],"published-print":{"date-parts":[[2018,7,31]]},"abstract":"<jats:p>Co-saliency detection is a newly emerging and rapidly growing research area in the computer vision community. As a novel branch of visual saliency, co-saliency detection refers to the discovery of common and salient foregrounds from two or more relevant images, and it can be widely used in many computer vision tasks. The existing co-saliency detection algorithms mainly consist of three components: extracting effective features to represent the image regions, exploring the informative cues or factors to characterize co-saliency, and designing effective computational frameworks to formulate co-saliency. Although numerous methods have been developed, the literature is still lacking a deep review and evaluation of co-saliency detection techniques. In this article, we aim at providing a comprehensive review of the fundamentals, challenges, and applications of co-saliency detection. Specifically, we provide an overview of some related computer vision works, review the history of co-saliency detection, summarize and categorize the major algorithms in this research area, discuss some open issues in this area, present the potential applications of co-saliency detection, and finally point out some unsolved challenges and promising future works. We expect this review to be beneficial to both fresh and senior researchers in this field and to give insights to researchers in other related areas regarding the utility of co-saliency detection algorithms.<\/jats:p>","DOI":"10.1145\/3158674","type":"journal-article","created":{"date-parts":[[2018,1,31]],"date-time":"2018-01-31T13:25:40Z","timestamp":1517405140000},"page":"1-31","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":96,"title":["A Review of Co-Saliency Detection Algorithms"],"prefix":"10.1145","volume":"9","author":[{"given":"Dingwen","family":"Zhang","sequence":"first","affiliation":[{"name":"Northwestern Polytechnical University, Xi'an, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Huazhu","family":"Fu","sequence":"additional","affiliation":[{"name":"Institute for Infocomm Research, Agency for Science, Technology and Research, Singapore"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5545-7217","authenticated-orcid":false,"given":"Junwei","family":"Han","sequence":"additional","affiliation":[{"name":"Northwestern Polytechnical University, Xi'an, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ali","family":"Borji","sequence":"additional","affiliation":[{"name":"University of Central Florida, Orlando, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xuelong","family":"Li","sequence":"additional","affiliation":[{"name":"Xi\u2019an Institute of Optics and Precision Mechanics, Chinese Academy of Sciences, Xi'an, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2018,1,30]]},"reference":[{"key":"e_1_2_1_1_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2009.5206596"},{"key":"e_1_2_1_2_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2010.5540080"},{"key":"e_1_2_1_3_1","doi-asserted-by":"publisher","DOI":"10.5555\/2354409.2354875"},{"key":"e_1_2_1_4_1","doi-asserted-by":"publisher","DOI":"10.1109\/TIP.2015.2487833"},{"key":"e_1_2_1_5_1","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2012.89"},{"key":"e_1_2_1_6_1","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2015.2480683"},{"key":"e_1_2_1_7_1","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2015.2496947"},{"key":"e_1_2_1_8_1","doi-asserted-by":"publisher","DOI":"10.1145\/2647868.2655007"},{"key":"e_1_2_1_9_1","first-page":"4175","article-title":"Self-adaptively weighted co-saliency detection via rank constraint","volume":"23","author":"Cao Xiaochun","year":"2014","unstructured":"Xiaochun Cao , Zhiqiang Tao , Bao Zhang , Huazhu Fu , and Wei Feng . 2014 . Self-adaptively weighted co-saliency detection via rank constraint . IEEE Transactions on Image Processing 23 , 9 (2014), 4175 -- 4186 . Xiaochun Cao, Zhiqiang Tao, Bao Zhang, Huazhu Fu, and Wei Feng. 2014. Self-adaptively weighted co-saliency detection via rank constraint. IEEE Transactions on Image Processing 23, 9 (2014), 4175--4186.","journal-title":"IEEE Transactions on Image Processing"},{"key":"e_1_2_1_10_1","volume-title":"IEEE International Conference on Multimedia and Expo","author":"Cao Xiaochun","year":"2013","unstructured":"Xiaochun Cao , Zhiqiang Tao , Bao Zhang , Huazhu Fu , and Xuewei Li . 2013 . Saliency map fusion based on rank-one constraint . In IEEE International Conference on Multimedia and Expo . San Jose, IEEE, 1--8. Xiaochun Cao, Zhiqiang Tao, Bao Zhang, Huazhu Fu, and Xuewei Li. 2013. Saliency map fusion based on rank-one constraint. In IEEE International Conference on Multimedia and Expo. San Jose, IEEE, 1--8."},{"key":"e_1_2_1_11_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.neucom.2014.12.105"},{"key":"e_1_2_1_12_1","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2015.2488637"},{"key":"e_1_2_1_13_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2011.5995415"},{"key":"e_1_2_1_14_1","doi-asserted-by":"publisher","DOI":"10.1109\/TCYB.2016.2539546"},{"key":"e_1_2_1_15_1","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2016.2608901"},{"key":"e_1_2_1_16_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICIP.2010.5650014"},{"key":"e_1_2_1_17_1","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2015.2506664"},{"key":"e_1_2_1_18_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICPR.2014.400"},{"key":"e_1_2_1_19_1","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2014.2345401"},{"key":"e_1_2_1_20_1","doi-asserted-by":"publisher","DOI":"10.1007\/s00371-013-0867-4"},{"key":"e_1_2_1_21_1","doi-asserted-by":"publisher","DOI":"10.1145\/2632856.2632866"},{"key":"e_1_2_1_22_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2015.7298724"},{"key":"e_1_2_1_23_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2014.309"},{"key":"e_1_2_1_24_1","doi-asserted-by":"publisher","DOI":"10.1109\/TIP.2017.2763819"},{"key":"e_1_2_1_25_1","volume-title":"An iterative co-saliency framework for RGBD images","author":"Cong Runmin","year":"2017","unstructured":"Runmin Cong , Jianjun Lei , Huazhu Fu , Weisi Lin , Qingming Huang , Xiaochun Cao , and Chunping Hou . 2017, Accepted. An iterative co-saliency framework for RGBD images . IEEE Transactions on Cybernetics ( 2017 , Accepted) . Runmin Cong, Jianjun Lei, Huazhu Fu, Weisi Lin, Qingming Huang, Xiaochun Cao, and Chunping Hou. 2017, Accepted. An iterative co-saliency framework for RGBD images. IEEE Transactions on Cybernetics (2017, Accepted)."},{"key":"e_1_2_1_26_1","doi-asserted-by":"publisher","DOI":"10.1145\/1143844.1143874"},{"key":"e_1_2_1_27_1","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2012.2235082"},{"key":"e_1_2_1_28_1","doi-asserted-by":"publisher","DOI":"10.1007\/s11263-012-0538-3"},{"key":"e_1_2_1_29_1","doi-asserted-by":"publisher","DOI":"10.1109\/LSP.2014.2347333"},{"key":"e_1_2_1_30_1","doi-asserted-by":"publisher","DOI":"10.1109\/TIP.2014.2336549"},{"key":"e_1_2_1_31_1","volume-title":"Finding Pictures of Objects in Large Collections of Images","author":"Forsyth David A.","unstructured":"David A. Forsyth , Jitendra Malik , Margaret M. Fleck , Hayit Greenspan , Thomas Leung , Serge Belongie , Chad Carson , and Chris Bregler . 1996. Finding Pictures of Objects in Large Collections of Images . Springer . David A. Forsyth, Jitendra Malik, Margaret M. Fleck, Hayit Greenspan, Thomas Leung, Serge Belongie, Chad Carson, and Chris Bregler. 1996. Finding Pictures of Objects in Large Collections of Images. Springer."},{"key":"e_1_2_1_32_1","doi-asserted-by":"publisher","DOI":"10.1109\/TIP.2013.2260166"},{"key":"e_1_2_1_33_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2015.7299072"},{"key":"e_1_2_1_34_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2014.405"},{"key":"e_1_2_1_35_1","doi-asserted-by":"publisher","DOI":"10.1109\/TIP.2015.2442915"},{"key":"e_1_2_1_36_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-540-88682-2_16"},{"key":"e_1_2_1_37_1","volume-title":"IEEE Conference on Computer Vision and Pattern Recognition","author":"Gan Chuang","unstructured":"Chuang Gan , Naiyan Wang , Yi Yang , Dit Yan Yeung , and Alexander G. Hauptmann . 2015. DevNet: A deep event network for multimedia event detection and evidence recounting . In IEEE Conference on Computer Vision and Pattern Recognition . Boston, IEEE, 2568--2577. Chuang Gan, Naiyan Wang, Yi Yang, Dit Yan Yeung, and Alexander G. Hauptmann. 2015. DevNet: A deep event network for multimedia event detection and evidence recounting. In IEEE Conference on Computer Vision and Pattern Recognition. Boston, IEEE, 2568--2577."},{"key":"e_1_2_1_38_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.image.2016.03.005"},{"key":"e_1_2_1_39_1","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2011.272"},{"key":"e_1_2_1_40_1","first-page":"1","article-title":"A unified metric learning-based framework for co-saliency detection","volume":"99","author":"Han Junwei","year":"2017","unstructured":"Junwei Han , Gong Cheng , Zhenpeng Li , and Dingwen Zhang . 2017 . A unified metric learning-based framework for co-saliency detection . IEEE Transactions on Circuits and Systems for Video Technology PP , 99 (2017), 1 -- 1 . Junwei Han, Gong Cheng, Zhenpeng Li, and Dingwen Zhang. 2017. A unified metric learning-based framework for co-saliency detection. IEEE Transactions on Circuits and Systems for Video Technology PP, 99 (2017), 1--1.","journal-title":"IEEE Transactions on Circuits and Systems for Video Technology PP"},{"key":"e_1_2_1_41_1","doi-asserted-by":"publisher","DOI":"10.1109\/TGRS.2014.2374218"},{"key":"e_1_2_1_42_1","doi-asserted-by":"publisher","DOI":"10.1109\/TCSVT.2014.2381471"},{"key":"e_1_2_1_43_1","doi-asserted-by":"publisher","DOI":"10.1109\/TCYB.2015.2404432"},{"key":"e_1_2_1_44_1","volume-title":"IEEE Conference on Computer Vision and Pattern Recognition","author":"Dorit","unstructured":"Dorit S. Hochbaum and Vikas Singh. 2009. An efficient algorithm for co-segmentation . In IEEE Conference on Computer Vision and Pattern Recognition . Miami, IEEE, 269--276. Dorit S. Hochbaum and Vikas Singh. 2009. An efficient algorithm for co-segmentation. In IEEE Conference on Computer Vision and Pattern Recognition. Miami, IEEE, 269--276."},{"key":"e_1_2_1_45_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICME.2015.7177414"},{"key":"e_1_2_1_46_1","doi-asserted-by":"publisher","DOI":"10.5555\/2919332.2919806"},{"key":"e_1_2_1_47_1","doi-asserted-by":"publisher","DOI":"10.1109\/34.730558"},{"key":"e_1_2_1_48_1","doi-asserted-by":"publisher","DOI":"10.1145\/1866029.1866066"},{"key":"e_1_2_1_49_1","unstructured":"Dong-ju Jeong Insung Hwang and Nam Ik Cho. 2017. Co-salient object detection based on deep saliency networks and seed propagation over an integrated graph. In arXiv:1706.09650.  Dong-ju Jeong Insung Hwang and Nam Ik Cho. 2017. Co-salient object detection based on deep saliency networks and seed propagation over an integrated graph. In arXiv:1706.09650."},{"key":"e_1_2_1_50_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-46478-7_12"},{"key":"e_1_2_1_51_1","doi-asserted-by":"publisher","DOI":"10.1587\/transinf.2014EDL8172"},{"key":"e_1_2_1_52_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2010.5539868"},{"key":"e_1_2_1_53_1","doi-asserted-by":"publisher","DOI":"10.5555\/2354409.2354929"},{"key":"e_1_2_1_54_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-10599-4_17"},{"key":"e_1_2_1_55_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2015.7298807"},{"key":"e_1_2_1_56_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.1999.784638"},{"key":"e_1_2_1_57_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2011.6126239"},{"key":"e_1_2_1_58_1","doi-asserted-by":"publisher","DOI":"10.1109\/TCSVT.2011.2125450"},{"key":"e_1_2_1_59_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-642-33709-3_8"},{"key":"e_1_2_1_60_1","doi-asserted-by":"publisher","DOI":"10.1109\/TMM.2013.2271476"},{"key":"e_1_2_1_61_1","doi-asserted-by":"publisher","DOI":"10.1109\/TIP.2011.2156803"},{"key":"e_1_2_1_62_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICME.2014.6890183"},{"key":"e_1_2_1_63_1","doi-asserted-by":"publisher","DOI":"10.1007\/s11263-013-0660-x"},{"key":"e_1_2_1_64_1","doi-asserted-by":"publisher","DOI":"10.1109\/TIP.2013.2253483"},{"key":"e_1_2_1_65_1","doi-asserted-by":"publisher","DOI":"10.1145\/2508037.2508039"},{"key":"e_1_2_1_66_1","doi-asserted-by":"publisher","DOI":"10.1109\/LSP.2014.2364896"},{"key":"e_1_2_1_67_1","doi-asserted-by":"publisher","DOI":"10.1109\/TCSVT.2013.2270367"},{"key":"e_1_2_1_68_1","first-page":"1","article-title":"Learning to segment human by watching YouTube","volume":"99","author":"Liang Xiaodan","year":"2017","unstructured":"Xiaodan Liang , Yunchao Wei , YunPeng Chen , Jianchao Yang , Liang Lin , and Shuicheng Yan . 2017 . Learning to segment human by watching YouTube . IEEE Transactions on Pattern Analysis and Machine Intelligence PP , 99 (2017), 1 -- 1 . Xiaodan Liang, Yunchao Wei, YunPeng Chen, Jianchao Yang, Liang Lin, and Shuicheng Yan. 2017. Learning to segment human by watching YouTube. IEEE Transactions on Pattern Analysis and Machine Intelligence PP, 99 (2017), 1--1.","journal-title":"IEEE Transactions on Pattern Analysis and Machine Intelligence PP"},{"key":"e_1_2_1_69_1","doi-asserted-by":"publisher","DOI":"10.1109\/TCYB.2016.2608906"},{"key":"e_1_2_1_70_1","volume-title":"IEEE Conference on Computer Vision and Pattern Recognition","author":"Liu Nian","year":"2015","unstructured":"Nian Liu , Junwei Han , Dingwen Zhang , Shifeng Wen , and Tianming Liu . 2015 . Predicting eye fixations using convolutional neural networks . In IEEE Conference on Computer Vision and Pattern Recognition . Boston, IEEE, 362--370. Nian Liu, Junwei Han, Dingwen Zhang, Shifeng Wen, and Tianming Liu. 2015. Predicting eye fixations using convolutional neural networks. In IEEE Conference on Computer Vision and Pattern Recognition. Boston, IEEE, 362--370."},{"key":"e_1_2_1_71_1","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2010.70"},{"key":"e_1_2_1_72_1","doi-asserted-by":"publisher","DOI":"10.1109\/LSP.2013.2292873"},{"key":"e_1_2_1_73_1","doi-asserted-by":"publisher","DOI":"10.1109\/ACPR.2017.91"},{"key":"e_1_2_1_74_1","doi-asserted-by":"publisher","DOI":"10.1145\/2502081.2502240"},{"key":"e_1_2_1_75_1","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2015.2392783"},{"key":"e_1_2_1_76_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.jvcir.2016.02.001"},{"key":"e_1_2_1_77_1","doi-asserted-by":"publisher","DOI":"10.5555\/1687044.1687119"},{"key":"e_1_2_1_78_1","doi-asserted-by":"publisher","DOI":"10.21236\/ADA507101"},{"key":"e_1_2_1_79_1","doi-asserted-by":"publisher","DOI":"10.1145\/2502081.2502128"},{"key":"e_1_2_1_80_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2011.6126383"},{"key":"e_1_2_1_81_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.dsp.2014.09.005"},{"key":"e_1_2_1_82_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-10578-9_7"},{"key":"e_1_2_1_83_1","doi-asserted-by":"publisher","DOI":"10.5555\/2354409.2354924"},{"key":"e_1_2_1_84_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.81"},{"key":"e_1_2_1_85_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-642-37431-9_45"},{"key":"e_1_2_1_86_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2006.91"},{"key":"e_1_2_1_87_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2013.253"},{"key":"e_1_2_1_88_1","doi-asserted-by":"publisher","DOI":"10.1007\/s11263-015-0816-y"},{"key":"e_1_2_1_89_1","doi-asserted-by":"publisher","DOI":"10.1142\/S021800141655003X"},{"key":"e_1_2_1_90_1","first-page":"1","article-title":"Transfer learning by ranking for weakly supervised object annotation. In British Machine Vision Conference","volume":"78","author":"Shi Zhiyuan","year":"2012","unstructured":"Zhiyuan Shi , Parthipan Siva , Tony Xiang , and Q. Mary . 2012 . Transfer learning by ranking for weakly supervised object annotation. In British Machine Vision Conference . Guildford , 78 . 1 -- 78 .11. Zhiyuan Shi, Parthipan Siva, Tony Xiang, and Q. Mary. 2012. Transfer learning by ranking for weakly supervised object annotation. In British Machine Vision Conference. Guildford, 78.1--78.11.","journal-title":"Guildford"},{"key":"e_1_2_1_91_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2013.416"},{"key":"e_1_2_1_92_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2011.6126261"},{"key":"e_1_2_1_93_1","doi-asserted-by":"publisher","DOI":"10.1109\/LSP.2016.2615293"},{"key":"e_1_2_1_94_1","volume-title":"Stefanie Jegelka, and Trevor Darrell.","author":"Song Hyun Oh","year":"2014","unstructured":"Hyun Oh Song , Yong Jae Lee , Stefanie Jegelka, and Trevor Darrell. 2014 . Weakly-supervised discovery of visual pattern configurations. In Advances in Neural Information Processing Systems. Montreal , 1637--1645. Hyun Oh Song, Yong Jae Lee, Stefanie Jegelka, and Trevor Darrell. 2014. Weakly-supervised discovery of visual pattern configurations. In Advances in Neural Information Processing Systems. Montreal, 1637--1645."},{"key":"e_1_2_1_95_1","doi-asserted-by":"publisher","DOI":"10.1145\/2533989"},{"key":"e_1_2_1_96_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICASSP.2013.6638027"},{"key":"e_1_2_1_97_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2014.190"},{"key":"e_1_2_1_98_1","volume-title":"AAAI Conference on Artificial Intelligence","author":"Tao Zhiqiang","year":"2017","unstructured":"Zhiqiang Tao , Hongfu Liu , Huazhu Fu , and Yun Fu . 2017 . Image cosegmentation via saliency-guided constraint clustering with cosine similarity . In AAAI Conference on Artificial Intelligence . San Francisco, 4285--4291. Zhiqiang Tao, Hongfu Liu, Huazhu Fu, and Yun Fu. 2017. Image cosegmentation via saliency-guided constraint clustering with cosine similarity. In AAAI Conference on Artificial Intelligence. San Francisco, 4285--4291."},{"key":"e_1_2_1_99_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2014.13"},{"key":"e_1_2_1_100_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2011.5995530"},{"key":"e_1_2_1_101_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-10599-4_28"},{"key":"e_1_2_1_102_1","doi-asserted-by":"publisher","DOI":"10.1109\/TIP.2015.2438550"},{"key":"e_1_2_1_103_1","volume-title":"IEEE Conference on Computer Vision and Pattern Recognition","author":"Wang Wenguan","unstructured":"Wenguan Wang , Jianbing Shen , and F. Porikli . 2015. Saliency-aware geodesic video object segmentation . In IEEE Conference on Computer Vision and Pattern Recognition . Boston, IEEE, 3395--3402. Wenguan Wang, Jianbing Shen, and F. Porikli. 2015. Saliency-aware geodesic video object segmentation. In IEEE Conference on Computer Vision and Pattern Recognition. Boston, IEEE, 3395--3402."},{"key":"e_1_2_1_104_1","volume-title":"ViCoS2: Video co-saliency guided co-segmentation","author":"Wang Wenguan","year":"2017","unstructured":"Wenguan Wang , Jianbing Shen , Hanqiu Sun , and Ling Shao . 2017. ViCoS2: Video co-saliency guided co-segmentation . IEEE Transactions on Circuits and Systems for Video Technology ( 2017 ). Wenguan Wang, Jianbing Shen, Hanqiu Sun, and Ling Shao. 2017. ViCoS2: Video co-saliency guided co-segmentation. IEEE Transactions on Circuits and Systems for Video Technology (2017)."},{"key":"e_1_2_1_105_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2013.56"},{"key":"e_1_2_1_106_1","doi-asserted-by":"publisher","DOI":"10.5555\/3172077.3172313"},{"key":"e_1_2_1_107_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-642-33712-3_3"},{"key":"e_1_2_1_108_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2005.171"},{"key":"e_1_2_1_109_1","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2015.2512898"},{"key":"e_1_2_1_110_1","volume-title":"International Conference on Digital Image Processing. Chengdu, SPIE, 100335G.","author":"Xie Yufeng","year":"2016","unstructured":"Yufeng Xie , Linwei Ye , Zhi Liu , and Xuemei Zou . 2016 . Video co-saliency detection . In International Conference on Digital Image Processing. Chengdu, SPIE, 100335G. Yufeng Xie, Linwei Ye, Zhi Liu, and Xuemei Zou. 2016. Video co-saliency detection. In International Conference on Digital Image Processing. Chengdu, SPIE, 100335G."},{"key":"e_1_2_1_111_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2013.153"},{"key":"e_1_2_1_112_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2013.407"},{"key":"e_1_2_1_113_1","doi-asserted-by":"publisher","DOI":"10.1109\/TCSVT.2015.2433171"},{"key":"e_1_2_1_114_1","doi-asserted-by":"publisher","DOI":"10.1109\/TMM.2011.2162399"},{"key":"e_1_2_1_115_1","doi-asserted-by":"publisher","DOI":"10.1109\/TIP.2017.2694222"},{"key":"e_1_2_1_116_1","doi-asserted-by":"publisher","DOI":"10.1109\/LSP.2015.2458434"},{"key":"e_1_2_1_117_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2017.200"},{"key":"e_1_2_1_118_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2011.6126474"},{"key":"e_1_2_1_119_1","doi-asserted-by":"publisher","DOI":"10.1109\/LGRS.2014.2358994"},{"key":"e_1_2_1_120_1","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2015.2495161"},{"key":"e_1_2_1_121_1","doi-asserted-by":"publisher","DOI":"10.1109\/TIP.2017.2658957"},{"key":"e_1_2_1_122_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2015.7298918"},{"key":"e_1_2_1_123_1","doi-asserted-by":"publisher","DOI":"10.1007\/s11263-016-0907-4"},{"key":"e_1_2_1_124_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2017.382"},{"key":"e_1_2_1_125_1","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2016.2567393"},{"key":"e_1_2_1_126_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2015.75"},{"key":"e_1_2_1_127_1","volume-title":"International Joint Conference on Artificial Intelligence","author":"Zhang Dingwen","year":"2016","unstructured":"Dingwen Zhang , Deyu Meng , Long Zhao , and Junwei Han . 2016 . Bridging saliency detection to weakly supervised object detection based on self-paced curriculum learning . In International Joint Conference on Artificial Intelligence . New York, 3538--3544. Dingwen Zhang, Deyu Meng, Long Zhao, and Junwei Han. 2016. Bridging saliency detection to weakly supervised object detection based on self-paced curriculum learning. In International Joint Conference on Artificial Intelligence. New York, 3538--3544."},{"key":"e_1_2_1_128_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2017.567"},{"key":"e_1_2_1_129_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2015.165"},{"key":"e_1_2_1_130_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-63558-3_24"},{"key":"e_1_2_1_131_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.jvcir.2015.10.012"},{"key":"e_1_2_1_132_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2014.360"},{"key":"e_1_2_1_133_1","doi-asserted-by":"publisher","DOI":"10.1145\/2629483"}],"container-title":["ACM Transactions on Intelligent Systems and Technology"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3158674","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3158674","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,18]],"date-time":"2025-06-18T02:26:10Z","timestamp":1750213570000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3158674"}},"subtitle":["Fundamentals, Applications, and Challenges"],"short-title":[],"issued":{"date-parts":[[2018,1,30]]},"references-count":133,"journal-issue":{"issue":"4","published-print":{"date-parts":[[2018,7,31]]}},"alternative-id":["10.1145\/3158674"],"URL":"https:\/\/doi.org\/10.1145\/3158674","relation":{},"ISSN":["2157-6904","2157-6912"],"issn-type":[{"value":"2157-6904","type":"print"},{"value":"2157-6912","type":"electronic"}],"subject":[],"published":{"date-parts":[[2018,1,30]]},"assertion":[{"value":"2017-07-01","order":0,"name":"received","label":"Received","group":{"name":"publication_history","label":"Publication History"}},{"value":"2017-11-01","order":1,"name":"accepted","label":"Accepted","group":{"name":"publication_history","label":"Publication History"}},{"value":"2018-01-30","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}