{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,2,21]],"date-time":"2025-02-21T07:24:32Z","timestamp":1740122672861,"version":"3.37.3"},"reference-count":66,"publisher":"Springer Science and Business Media LLC","issue":"20","license":[{"start":{"date-parts":[[2024,7,20]],"date-time":"2024-07-20T00:00:00Z","timestamp":1721433600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,7,20]],"date-time":"2024-07-20T00:00:00Z","timestamp":1721433600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"DOI":"10.13039\/100007219","name":"Natural Science Foundation of Shanghai Municipality","doi-asserted-by":"publisher","award":["21ZR1462600"],"award-info":[{"award-number":["21ZR1462600"]}],"id":[{"id":"10.13039\/100007219","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Appl Intell"],"published-print":{"date-parts":[[2024,10]]},"DOI":"10.1007\/s10489-024-05694-6","type":"journal-article","created":{"date-parts":[[2024,7,20]],"date-time":"2024-07-20T07:01:53Z","timestamp":1721458913000},"page":"9685-9705","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Detecting camouflaged objects via cross-level context supplement"],"prefix":"10.1007","volume":"54","author":[{"given":"Qing","family":"Zhang","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Weiqi","family":"Yan","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Rui","family":"Zhao","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yanjiao","family":"Shi","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jian","family":"Zeng","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2024,7,20]]},"reference":[{"key":"5694_CR1","doi-asserted-by":"crossref","unstructured":"Fan D-P, Ji G-P, Sun G, Cheng M-M, Shen J, Shao L (2020) Camouflaged object detection. In: Proceedings of IEEE conference on computer vision and pattern recognition, pp 2774\u20132784","DOI":"10.1109\/CVPR42600.2020.00285"},{"key":"5694_CR2","doi-asserted-by":"crossref","unstructured":"Mei H, Ji G-P, Wei Z, Yang X, Wei X, Fan D-P (2021) Camouflaged object segmentation with distraction mining. In: Proceedings of IEEE conference on computer vision and pattern recognition, pp 8768\u20138777","DOI":"10.1109\/CVPR46437.2021.00866"},{"key":"5694_CR3","doi-asserted-by":"crossref","unstructured":"Pang Y, Zhao X, Xiang T-Z, Zhan, L, Lu H (2022) Zoom in and out: a mixed-scale triplet network for camouflaged object detection. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 2160\u20132170","DOI":"10.1109\/CVPR52688.2022.00220"},{"key":"5694_CR4","doi-asserted-by":"crossref","unstructured":"He C, Li K, Zhang Y, Tang L, Zhang Y, Guo Z, Li X (2023) Camouflaged object detection with feature decomposition and edge reconstruction. In: Proceedings of IEEE conference on computer vision and pattern recognition, pp 22046\u201322055","DOI":"10.1109\/CVPR52729.2023.02111"},{"key":"5694_CR5","unstructured":"Lyu Y, Zhang H, Li Y, Liu H, Yang Y, Yuan D (2023) UEDG: uncertainty-edge dual guided camouflage object detection. IEEE Transactions on Multimedia, 1\u201311"},{"key":"5694_CR6","doi-asserted-by":"crossref","unstructured":"Sun Y, Wang S, Chen C, Xiang T-Z (2022) Boundary-guided camouflaged object detection. In: Proceedings of international joint conferences on artificial intelligence, pp 1335\u20131341","DOI":"10.24963\/ijcai.2022\/186"},{"key":"5694_CR7","doi-asserted-by":"crossref","unstructured":"Zhu H, Li P, Xie H, Yan X, Liang D, Chen D, Wei M, Qin J (2022) I can find you! boundary-guided separated attention network for camouflaged object detection. In: Proceedings of AAAI conference on artificial intelligence, pp 3608\u20133616","DOI":"10.1609\/aaai.v36i3.20273"},{"issue":"10","key":"5694_CR8","doi-asserted-by":"publisher","first-page":"6981","DOI":"10.1109\/TCSVT.2022.3178173","volume":"32","author":"G Chen","year":"2022","unstructured":"Chen G, Liu S-J, Sun Y-J, Ji G-P, Wu Y-F, Zhou T (2022) Camouflaged object detection via context-aware cross-level fusion. IEEE Trans Circuits Syst Video Technol 32(10):6981\u20136993","journal-title":"IEEE Trans Circuits Syst Video Technol"},{"key":"5694_CR9","doi-asserted-by":"crossref","unstructured":"Zhai W, Cao Y, Xie H, Zha Z-J (2022) Deep texton-coherence network for camouflaged object detection. IEEE Transactions on Multimedia, 1\u201311","DOI":"10.1109\/TMM.2022.3188401"},{"key":"5694_CR10","doi-asserted-by":"crossref","unstructured":"Zhao J-X, Liu J-J, Fan D-P, Cao Y, Yang J, Cheng M-M (2019) Egnet: Edge guidance network for salient object detection. In: Proceedings of international conference on computer vision, pp 8779\u20138788","DOI":"10.1109\/ICCV.2019.00887"},{"key":"5694_CR11","doi-asserted-by":"crossref","unstructured":"Liu J, Hou Q, Cheng M, Feng J, Jiang J (2019) A simple pooling-based design for real-time salient object detection. In: Proceedings of international conference on computer vision and pattern recognition, pp 3917\u20133926","DOI":"10.1109\/CVPR.2019.00404"},{"key":"5694_CR12","doi-asserted-by":"publisher","DOI":"10.1016\/j.patcog.2021.108414","volume":"123","author":"G-P Ji","year":"2022","unstructured":"Ji G-P, Zhu L, Zhuge M, Fu K (2022) Fast camouflaged object detection via edge-based reversible re-calibration network. Pattern Recogn 123:108414","journal-title":"Pattern Recogn"},{"key":"5694_CR13","doi-asserted-by":"publisher","first-page":"7036","DOI":"10.1109\/TIP.2022.3217695","volume":"31","author":"T Zhou","year":"2022","unstructured":"Zhou T, Zhou Y, Gong C, Yang J, Zhang Y (2022) Feature aggregation and propagation network for camouflaged object detection. IEEE Trans Image Process 31:7036\u20137047","journal-title":"IEEE Trans Image Process"},{"key":"5694_CR14","doi-asserted-by":"publisher","first-page":"45","DOI":"10.1016\/j.cviu.2019.04.006","volume":"184","author":"T-N Le","year":"2019","unstructured":"Le T-N, Nguyen TV, Nie Z, Tran M-T, Sugimoto A (2019) Anabranch network for camouflaged object segmentation. Comput Vis Image Underst 184:45\u201356","journal-title":"Comput Vis Image Underst"},{"key":"5694_CR15","doi-asserted-by":"crossref","unstructured":"Lv Y, Zhang J, Dai Y, Li A, Liu B, Barnes N, Fan D-P (2021) Simultaneously localize, segment and rank the camouflaged objects. In: Proceedings of IEEE conference on computer vision and pattern recognition, pp 11586\u201311596","DOI":"10.1109\/CVPR46437.2021.01142"},{"key":"5694_CR16","doi-asserted-by":"crossref","unstructured":"Zhai Q, Li X, Yang F, Chen C, Cheng H, Fan D-P (2021) Mutual graph learning for camouflaged object detection. In: Proceedings of IEEE conference on computer vision and pattern recognition, pp 12992\u201313002","DOI":"10.1109\/CVPR46437.2021.01280"},{"key":"5694_CR17","doi-asserted-by":"crossref","unstructured":"Li A, Zhang J, Lv Y, Liu B, Zhang T, Dai Y (2021) Uncertainty-aware joint salient object and camouflaged object detection. In: IEEE\/CVF conference on computer vision and pattern recognition, pp 10066\u201310076","DOI":"10.1109\/CVPR46437.2021.00994"},{"key":"5694_CR18","doi-asserted-by":"crossref","unstructured":"Yang F, Zhai Q, Li X, Huang R, Luo A, Cheng H, Fan D-P (2021) Uncertainty-guided transformer reasoning for camouflaged object detection. In: Proceedings of IEEE international conference on computer vision, pp 4126\u20134135","DOI":"10.1109\/ICCV48922.2021.00411"},{"key":"5694_CR19","doi-asserted-by":"publisher","DOI":"10.1016\/j.patcog.2022.108644","volume":"127","author":"M Zhuge","year":"2022","unstructured":"Zhuge M, Lu X, Guo Y, Cai Z, Chen S (2022) Cubenet: X-shape connection for camouflaged object detection. Pattern Recogn 127:108644","journal-title":"Pattern Recogn"},{"key":"5694_CR20","doi-asserted-by":"crossref","unstructured":"Zhang M, Xu S, Piao Y, Shi D, Lin SL, Lu H (2022) Preynet: preying on camouflaged objects. In: Proceedings of ACM international conference on multimedia, pp 5323\u20135332","DOI":"10.1145\/3503161.3548178"},{"key":"5694_CR21","doi-asserted-by":"crossref","unstructured":"Jia Q, Yao S, Liu Y, Fan X, Liu R, Luo Z (2022) Segment, magnify and reiterate: detecting camouflaged objects the hard way. In: IEEE conference on computer vision and pattern recognition, pp 4703\u20134712","DOI":"10.1109\/CVPR52688.2022.00467"},{"issue":"9","key":"5694_CR22","doi-asserted-by":"publisher","first-page":"5708","DOI":"10.1109\/TCSVT.2021.3124952","volume":"32","author":"H Bi","year":"2022","unstructured":"Bi H, Zhang C, Wang K, Tong J, Zheng F (2022) Rethinking camouflaged object detection: models and datasets. IEEE Trans Circuits Syst Video Technol 32(9):5708\u20135724","journal-title":"IEEE Trans Circuits Syst Video Technol"},{"key":"5694_CR23","doi-asserted-by":"crossref","unstructured":"Sun Y, Chen G, Zhou T, Zhang Y, Liu N (2021) Context-aware cross-level fusion network for camouflaged object detection. In: Proceedings of international joint conferences on artificial intelligence, pp 1025\u20131031","DOI":"10.24963\/ijcai.2021\/142"},{"key":"5694_CR24","doi-asserted-by":"crossref","unstructured":"Chen Z, Zhu L, Wan L, Wang S, Feng W, Heng P-A (2020) A multi-task mean teacher for semi-supervised shadow detection. In: IEEE conference on computer vision and pattern recognition, pp 5610\u20135619","DOI":"10.1109\/CVPR42600.2020.00565"},{"key":"5694_CR25","doi-asserted-by":"crossref","unstructured":"Lee HJ, Kim JU, Lee S, Kim HG, Ro YM (2020) Structure boundary preserving segmentation for medical image with ambiguous boundary. In: IEEE conference on computer vision and pattern recognition, pp 4816\u20134825","DOI":"10.1109\/CVPR42600.2020.00487"},{"key":"5694_CR26","doi-asserted-by":"crossref","unstructured":"He K, Zhang X, Ren S, Sun J (2016) Deep residual learning for image recognition. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 770\u2013778","DOI":"10.1109\/CVPR.2016.90"},{"issue":"2","key":"5694_CR27","doi-asserted-by":"publisher","first-page":"652","DOI":"10.1109\/TPAMI.2019.2938758","volume":"43","author":"S-H Gao","year":"2021","unstructured":"Gao S-H, Cheng M-M, Zhao K, Zhang X-Y, Yang M-H, Torr P (2021) Res2net: a new multi-scale backbone architecture. IEEE Trans Pattern Anal Mach Intell 43(2):652\u2013662","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"5694_CR28","doi-asserted-by":"crossref","unstructured":"Peng C, Zhang X, Yu G, Luo G, Sun J (2017) Large kernel matters\u2013improving semantic segmentation by global convolutional network. In: Proceedings of IEEE conference on computer vision and pattern recognition, pp 1743\u20131751","DOI":"10.1109\/CVPR.2017.189"},{"key":"5694_CR29","doi-asserted-by":"crossref","unstructured":"Hu J, Shen L, Sun G (2018) Squeeze-and-excitation networks. In: Proceedings of IEEE conference on computer vision and pattern recognition, pp 7132\u20137141","DOI":"10.1109\/CVPR.2018.00745"},{"key":"5694_CR30","unstructured":"Przemys\u0142aw S, Hassan A, Jakub B, Tomasz D, Adam K, Kozie\u0142 P (2017) Animal camouflage analysis: Chameleon database. http:\/\/kgwisc.aei.polsl.pl\/index.php\/pl\/dataset\/63-animal-camouflage-analysis, ???"},{"key":"5694_CR31","doi-asserted-by":"crossref","unstructured":"Zhong Y, Li B, Tang L, Kuang S, Wu S, Ding S (2022) Detecting camouflaged object in frequency domain. In: IEEE\/CVF conference on computer vision and pattern recognition, pp 4494\u20134503","DOI":"10.1109\/CVPR52688.2022.00446"},{"key":"5694_CR32","doi-asserted-by":"crossref","unstructured":"Achanta R, Hemami S, Estrada FJ, Susstrunk S (2009) Frequency-tuned salient region detection. In: Proceedings of international conference on computer vision and pattern recognition, pp 1597\u20131604","DOI":"10.1109\/CVPRW.2009.5206596"},{"key":"5694_CR33","doi-asserted-by":"crossref","unstructured":"Margolin R, Zelnik-Manor L, Tal A (2014) How to evaluate foreground maps. In: Proceedings of international conference on computer vision and pattern recognition, pp 248\u2013255","DOI":"10.1109\/CVPR.2014.39"},{"key":"5694_CR34","doi-asserted-by":"crossref","unstructured":"Fan D, Cheng M, Liu Y, Li T, Botji A (2017) Structure-measure: a new way to evaluate foreground maps. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 4548\u20134557","DOI":"10.1109\/ICCV.2017.487"},{"key":"5694_CR35","doi-asserted-by":"crossref","unstructured":"Fan D-P, Gong C, Cao Y, Ren B, Cheng M-M, Borji A (2018) Enhanced-alignment measure for binary foreground map evaluation. In: Proceedings of international joint conference on artificial intelligence, pp 698\u2013704","DOI":"10.24963\/ijcai.2018\/97"},{"key":"5694_CR36","doi-asserted-by":"crossref","unstructured":"Wang Q, Wu B, Zhu P, Li P, Zuo W, Hu Q (2020) Eca-net: efficient channel attention for deep convolutional neural networks. In: Proceedings of IEEE conference on computer vision and pattern recognition, pp 11531\u201311539","DOI":"10.1109\/CVPR42600.2020.01155"},{"key":"5694_CR37","doi-asserted-by":"crossref","unstructured":"Woo S, Park J, Lee J, Kweon I (2018) Cbam: convolutional block attention module. In: Proceedings of European conference on computer vision, pp 3\u201319","DOI":"10.1007\/978-3-030-01234-2_1"},{"key":"5694_CR38","doi-asserted-by":"crossref","unstructured":"Ronneberger O, Fischer P, Brox T (2015) U-net: convolutional networks for biomedical image segmentation. In: Proceedings of the medical image computing and computer-assisted intervention, pp 234\u2013241","DOI":"10.1007\/978-3-319-24574-4_28"},{"issue":"6","key":"5694_CR39","doi-asserted-by":"publisher","first-page":"1856","DOI":"10.1109\/TMI.2019.2959609","volume":"39","author":"Z Zhou","year":"2020","unstructured":"Zhou Z, Siddiquee MMR, Tajbakhsh N, Liang J (2020) Unet++: redesigning skip connections to exploit multiscale features in image segmentation. IEEE Trans Med Imaging 39(6):1856\u20131867","journal-title":"IEEE Trans Med Imaging"},{"key":"5694_CR40","doi-asserted-by":"crossref","unstructured":"Fang Y, Chen C, Yuan Y, Tong K-Y (2019) Selective feature aggregation network with area-boundary constraints for polyp segmentation. In: Proceedings of the medical image computing and computer assisted intervention, pp 302\u2013310","DOI":"10.1007\/978-3-030-32239-7_34"},{"key":"5694_CR41","doi-asserted-by":"crossref","unstructured":"Fan D-P, Ji G-P, Zhou T, Chen G, Fu H, Shen J, Shao L (2020) Pranet: parallel reverse attention network for polyp segmentation. In: Proceedings of medical image computing and computer assisted intervention, pp 263\u2013273","DOI":"10.1007\/978-3-030-59725-2_26"},{"key":"5694_CR42","doi-asserted-by":"crossref","unstructured":"Patel K, Bur AM, Wang G (2021) Enhanced u-net: a feature enhancement network for polyp segmentation. In: Proceedings of the conference on robots and vision, pp 181\u2013188","DOI":"10.1109\/CRV52889.2021.00032"},{"key":"5694_CR43","doi-asserted-by":"crossref","unstructured":"Zhang R, Li G, Li Z, Cui S, Qian D, Yu Y (2020) Adaptive context selection for polyp segmentation. In: Proceedings of medical image computing and computer assisted intervention, pp 253\u2013262","DOI":"10.1007\/978-3-030-59725-2_25"},{"key":"5694_CR44","doi-asserted-by":"crossref","unstructured":"Zhao X, Zhang L, Lu H (2021) Automatic polyp segmentation via multi-scale subtraction network. In: Proceedings of medical image computing and computer assisted intervention, pp 120\u2013130","DOI":"10.1007\/978-3-030-87193-2_12"},{"key":"5694_CR45","doi-asserted-by":"crossref","unstructured":"Wei J, Hu Y, Zhang R, Li Z, Zhou SK, Cui S (2021) Shallow attention network for polyp segmentation. In: International conference on medical image computing and computer-assisted intervention, pp 699\u2013708","DOI":"10.1007\/978-3-030-87193-2_66"},{"key":"5694_CR46","doi-asserted-by":"crossref","unstructured":"Pang Y, Zhao X, Zhang L, Lu H (2020) Multi-scale interactive network for salient object detection. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, pp 9413\u20139422","DOI":"10.1109\/CVPR42600.2020.00943"},{"key":"5694_CR47","doi-asserted-by":"crossref","unstructured":"Wei J, Wang S, Wu Z, Su C, Huang Q, Tian Q (2020) Label decoupling framework for salient object detection. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, pp 13025\u201313034","DOI":"10.1109\/CVPR42600.2020.01304"},{"key":"5694_CR48","doi-asserted-by":"publisher","first-page":"8652","DOI":"10.1109\/TIP.2020.3017352","volume":"29","author":"J-J Liu","year":"2020","unstructured":"Liu J-J, Hou Q, Cheng M-M (2020) Dynamic feature integration for simultaneous detection of salient object, edge, and skeleton. IEEE Trans Image Process 29:8652\u20138667","journal-title":"IEEE Trans Image Process"},{"key":"5694_CR49","doi-asserted-by":"publisher","first-page":"1397","DOI":"10.1109\/TMM.2020.2997192","volume":"23","author":"J Li","year":"2021","unstructured":"Li J, Pan Z, Liu Q, Wang Z (2021) Stacked u-shape network with channel-wise attention for salient object detection. IEEE Trans Multimedia 23:1397\u20131409","journal-title":"IEEE Trans Multimedia"},{"key":"5694_CR50","doi-asserted-by":"crossref","unstructured":"Ma M, Xia C, Li J (2021) Pyramid feature shrinking for salient object detection. In: Proceedings of the AAAI conference on artificial intelligence, pp 32311\u20132318","DOI":"10.1609\/aaai.v35i3.16331"},{"key":"5694_CR51","doi-asserted-by":"publisher","first-page":"6855","DOI":"10.1109\/TIP.2021.3099405","volume":"30","author":"J Li","year":"2021","unstructured":"Li J, Su J, Xia C, Ma M, Tian Y (2021) Salient object detection with purificatory mechanism and structural similarity loss. IEEE Trans Image Process 30:6855\u20136868","journal-title":"IEEE Trans Image Process"},{"key":"5694_CR52","doi-asserted-by":"publisher","first-page":"8426","DOI":"10.1109\/TIP.2021.3113794","volume":"30","author":"S Yang","year":"2021","unstructured":"Yang S, Lin W, Lin G, Jiang Q, Liu Z (2021) Progressive self-guided loss for salient object detection. IEEE Trans Image Process 30:8426\u20138438","journal-title":"IEEE Trans Image Process"},{"key":"5694_CR53","unstructured":"Zhang J, Xie J, Barnes N, Li P (2021) Learning generative vision transformer with energy-based latent space for saliency prediction. In: Proceedings of conference on neural information processing systems, pp 15448\u201315463"},{"issue":"3","key":"5694_CR54","doi-asserted-by":"publisher","first-page":"1378","DOI":"10.1109\/TCSVT.2021.3069848","volume":"32","author":"H Mei","year":"2022","unstructured":"Mei H, Liu Y, Wei Z, Zhou D, Wei X, Zhang Q, Yang X (2022) Exploring dense context for salient object detection. IEEE Trans Circuits Syst Video Technol 32(3):1378\u20131389","journal-title":"IEEE Trans Circuits Syst Video Technol"},{"key":"5694_CR55","doi-asserted-by":"crossref","unstructured":"Zhuge M, Fan D-P, Liu N, Zhang D, Xu D, Shao L (2022) Salient object detection via integrity learning. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1\u20131","DOI":"10.1109\/TPAMI.2022.3179526"},{"key":"5694_CR56","first-page":"1","volume":"2017","author":"V David","year":"2017","unstructured":"David V, Jorge B, Javier SF, Gloria FE, L\u00f3pez A, Adriana R, Michal D, Aaron C (2017) A benchmark for endoluminal scene segmentation of colonoscopy images. Journal of Healthcare Engineering. 2017:1\u20139","journal-title":"Journal of Healthcare Engineering."},{"key":"5694_CR57","doi-asserted-by":"publisher","first-page":"99","DOI":"10.1016\/j.compmedimag.2015.02.007","volume":"43","author":"J Bernal","year":"2015","unstructured":"Bernal J, Sanchez FJ, Fernandez-Esparrach G, Gil D, Rodriguez C, Vilarino F (2015) WM-DOVA maps for accurate polyp highlighting in colonoscopy: validation vs. saliency maps from physicians. Comput Med Imaging Graph 43:99\u2013111","journal-title":"Comput Med Imaging Graph"},{"key":"5694_CR58","doi-asserted-by":"crossref","unstructured":"Tajbakhsh N, Gurudu SR, Liang J (2015) Automatic polyp detection in colonoscopy videos using an ensemble of convolutional neural networks. In: Proceedings of IEEE international symposium on biomedical imaging, pp 79\u201383","DOI":"10.1109\/ISBI.2015.7163821"},{"key":"5694_CR59","doi-asserted-by":"crossref","unstructured":"Jha D, Smedsrud PH, Riegler MA, Halvorsen P, Lange T, Johansen D, Johansen HD (2020) Kvasir-seg: a segmented polyp dataset. In: MultiMedia modeling, pp 451\u2013462","DOI":"10.1007\/978-3-030-37734-2_37"},{"key":"5694_CR60","doi-asserted-by":"crossref","unstructured":"Wang L, Lu H, Wang Y, Feng M, Wang D, Yin B, Ruan X (2017) Learning to detect salient objects with image-elvel supervision. In: Proceedings of IEEE conference on computer vision and pattern recognition, pp 136\u2013145","DOI":"10.1109\/CVPR.2017.404"},{"key":"5694_CR61","doi-asserted-by":"crossref","unstructured":"Yan Q, Xu L, Shi J, Jia J (2013) Hierarchical saliency detcion. In: Proceedings of IEEE conference on computer vision and pattern recognition, pp 1155\u20131162","DOI":"10.1109\/CVPR.2013.153"},{"key":"5694_CR62","doi-asserted-by":"crossref","unstructured":"Yang C, Zhang L, Lu H, Ruan X, Yang M (2013) Saliency detection via graph-based manifold ranking. In: Proceedings of IEEE international conference on computer vision and pattern recognition, pp 3166\u20133173","DOI":"10.1109\/CVPR.2013.407"},{"key":"5694_CR63","unstructured":"Li G, Yu Y (2015) Visual saliency based on multiscale deep features. In: Proceedings of IEEE conference on computer vision and pattern recognition, pp 5455\u20135463"},{"key":"5694_CR64","doi-asserted-by":"crossref","unstructured":"Li Y, Hou X, Koch C, Rehg JM, Yuille AL (2014) The secrets of salient object segmentation. In: Proceedings of IEEE conference on computer vision and pattern recognition, pp 280\u2013287","DOI":"10.1109\/CVPR.2014.43"},{"key":"5694_CR65","doi-asserted-by":"crossref","unstructured":"Lee MS, Shin W, Han SW (2022) TRACER: Extreme attention guided salient object tracing network. Proceedings of the AAAI Conference on Artificial Intelligence 36(11):12993-12994","DOI":"10.1609\/aaai.v36i11.21633"},{"key":"5694_CR66","unstructured":"Yuan Y, Gao P, Tan XY (2023) M$$^3$$Net: Multilevel, Mixed and Multistage Attention Network for Salient Object Detection. arXiv preprint arXiv:2309.08365"}],"container-title":["Applied Intelligence"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10489-024-05694-6.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s10489-024-05694-6\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10489-024-05694-6.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,8,15]],"date-time":"2024-08-15T13:21:38Z","timestamp":1723728098000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s10489-024-05694-6"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,7,20]]},"references-count":66,"journal-issue":{"issue":"20","published-print":{"date-parts":[[2024,10]]}},"alternative-id":["5694"],"URL":"https:\/\/doi.org\/10.1007\/s10489-024-05694-6","relation":{},"ISSN":["0924-669X","1573-7497"],"issn-type":[{"type":"print","value":"0924-669X"},{"type":"electronic","value":"1573-7497"}],"subject":[],"published":{"date-parts":[[2024,7,20]]},"assertion":[{"value":"15 July 2024","order":1,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"20 July 2024","order":2,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors have no competing interests to declare that are relevant to the content of this article.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing Interests"}},{"value":"Not applicable.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethical and Informed Consent for Data Used"}}]}}