{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,16]],"date-time":"2026-07-16T16:12:11Z","timestamp":1784218331515,"version":"3.55.0"},"publisher-location":"New York, NY, USA","reference-count":61,"publisher":"ACM","license":[{"start":{"date-parts":[[2022,10,10]],"date-time":"2022-10-10T00:00:00Z","timestamp":1665360000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"funder":[{"name":"the Natural Science Foundation of Liaoning Province","award":["2021-MS-123"],"award-info":[{"award-number":["2021-MS-123"]}]},{"name":"the Central Government Guided Local Science and Technology Development Funds of Liaoning Province","award":["2022JH6\/100100028"],"award-info":[{"award-number":["2022JH6\/100100028"]}]},{"name":"the National Natural Science Foundation of China","award":["62172070, 61976035"],"award-info":[{"award-number":["62172070, 61976035"]}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2022,10,10]]},"DOI":"10.1145\/3503161.3548178","type":"proceedings-article","created":{"date-parts":[[2022,10,10]],"date-time":"2022-10-10T15:43:12Z","timestamp":1665416592000},"page":"5323-5332","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":108,"title":["PreyNet: Preying on Camouflaged Objects"],"prefix":"10.1145","author":[{"given":"Miao","family":"Zhang","sequence":"first","affiliation":[{"name":"Dalian University of Technology &amp; Key Lab for Ubiquitous Network and Service Software of Liaoning Province, Dalian , China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shuang","family":"Xu","sequence":"additional","affiliation":[{"name":"Dalian University of Technology, Dalian, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yongri","family":"Piao","sequence":"additional","affiliation":[{"name":"Dalian University of Technology, Dalian, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Dongxiang","family":"Shi","sequence":"additional","affiliation":[{"name":"Dalian University of Technology, Dalian, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shusen","family":"Lin","sequence":"additional","affiliation":[{"name":"Dalian University of Technology, Dalian, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Huchuan","family":"Lu","sequence":"additional","affiliation":[{"name":"Dalian University of Technology &amp; Pengcheng Lab, Dalian, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2022,10,10]]},"reference":[{"key":"e_1_3_2_2_1_1","volume-title":"International conference on machine learning. PMLR, 1613--1622","author":"Blundell Charles","year":"2015","unstructured":"Charles Blundell , Julien Cornebise , Koray Kavukcuoglu , and Daan Wierstra . 2015 . Weight uncertainty in neural network . In International conference on machine learning. PMLR, 1613--1622 . Charles Blundell, Julien Cornebise, Koray Kavukcuoglu, and Daan Wierstra. 2015. Weight uncertainty in neural network. In International conference on machine learning. PMLR, 1613--1622."},{"key":"e_1_3_2_2_2_1","volume-title":"Deeplab: Semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected crfs","author":"Chen Liang-Chieh","year":"2017","unstructured":"Liang-Chieh Chen , George Papandreou , Iasonas Kokkinos , Kevin Murphy , and Alan L Yuille . 2017 . Deeplab: Semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected crfs . IEEE transactions on pattern analysis and machine intelligence 40, 4 (2017), 834--848. Liang-Chieh Chen, George Papandreou, Iasonas Kokkinos, Kevin Murphy, and Alan L Yuille. 2017. Deeplab: Semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected crfs. IEEE transactions on pattern analysis and machine intelligence 40, 4 (2017), 834--848."},{"key":"e_1_3_2_2_3_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-58598-3_31"},{"key":"e_1_3_2_2_4_1","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v34i07.6633"},{"key":"e_1_3_2_2_5_1","doi-asserted-by":"publisher","DOI":"10.1145\/1778765.1778788"},{"key":"e_1_3_2_2_6_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2017.487"},{"key":"e_1_3_2_2_7_1","volume-title":"Enhanced-alignment measure for binary foreground map evaluation. arXiv preprint arXiv:1805.10421","author":"Fan Deng-Ping","year":"2018","unstructured":"Deng-Ping Fan , Cheng Gong , Yang Cao , Bo Ren , Ming-Ming Cheng , and Ali Borji . 2018. Enhanced-alignment measure for binary foreground map evaluation. arXiv preprint arXiv:1805.10421 ( 2018 ). Deng-Ping Fan, Cheng Gong, Yang Cao, Bo Ren, Ming-Ming Cheng, and Ali Borji. 2018. Enhanced-alignment measure for binary foreground map evaluation. arXiv preprint arXiv:1805.10421 (2018)."},{"key":"e_1_3_2_2_8_1","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2021.3085766"},{"key":"e_1_3_2_2_9_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR42600.2020.00285"},{"key":"e_1_3_2_2_10_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-59725-2_26"},{"key":"e_1_3_2_2_11_1","doi-asserted-by":"publisher","DOI":"10.1109\/TMI.2020.2996645"},{"key":"e_1_3_2_2_12_1","volume-title":"international conference on machine learning. PMLR, 1050--1059","author":"Gal Yarin","year":"2016","unstructured":"Yarin Gal and Zoubin Ghahramani . 2016 . Dropout as a bayesian approximation: Representing model uncertainty in deep learning . In international conference on machine learning. PMLR, 1050--1059 . Yarin Gal and Zoubin Ghahramani. 2016. Dropout as a bayesian approximation: Representing model uncertainty in deep learning. In international conference on machine learning. PMLR, 1050--1059."},{"key":"e_1_3_2_2_13_1","volume-title":"Res2net: A new multi-scale backbone architecture","author":"Gao Shang-Hua","year":"2019","unstructured":"Shang-Hua Gao , Ming-Ming Cheng , Kai Zhao , Xin-Yu Zhang , Ming-Hsuan Yang , and Philip Torr . 2019. Res2net: A new multi-scale backbone architecture . IEEE transactions on pattern analysis and machine intelligence 43, 2 ( 2019 ), 652--662. Shang-Hua Gao, Ming-Ming Cheng, Kai Zhao, Xin-Yu Zhang, Ming-Hsuan Yang, and Philip Torr. 2019. Res2net: A new multi-scale backbone architecture. IEEE transactions on pattern analysis and machine intelligence 43, 2 (2019), 652--662."},{"key":"e_1_3_2_2_14_1","volume-title":"Learning a no-reference quality assessment model of enhanced images with big data","author":"Gu Ke","year":"2017","unstructured":"Ke Gu , Dacheng Tao , Jun-Fei Qiao , and Weisi Lin . 2017. Learning a no-reference quality assessment model of enhanced images with big data . IEEE transactions on neural networks and learning systems 29, 4 ( 2017 ), 1301--1313. Ke Gu, Dacheng Tao, Jun-Fei Qiao, and Weisi Lin. 2017. Learning a no-reference quality assessment model of enhanced images with big data. IEEE transactions on neural networks and learning systems 29, 4 (2017), 1301--1313."},{"key":"e_1_3_2_2_15_1","volume-title":"The analysis of image contrast: From quality assessment to automatic enhancement","author":"Gu Ke","year":"2015","unstructured":"Ke Gu , Guangtao Zhai , Weisi Lin , and Min Liu . 2015. The analysis of image contrast: From quality assessment to automatic enhancement . IEEE transactions on cybernetics 46, 1 ( 2015 ), 284--297. Ke Gu, Guangtao Zhai, Weisi Lin, and Min Liu. 2015. The analysis of image contrast: From quality assessment to automatic enhancement. IEEE transactions on cybernetics 46, 1 (2015), 284--297."},{"key":"e_1_3_2_2_16_1","doi-asserted-by":"publisher","DOI":"10.1109\/TIP.2015.2439035"},{"key":"e_1_3_2_2_17_1","doi-asserted-by":"crossref","first-page":"1480","DOI":"10.1109\/TCSVT.2014.2372392","article-title":"Automatic contrast enhancement technology with saliency preservation","volume":"25","author":"Gu Ke","year":"2014","unstructured":"Ke Gu , Guangtao Zhai , Xiaokang Yang , Wenjun Zhang , and Chang Wen Chen . 2014 . Automatic contrast enhancement technology with saliency preservation . IEEE Transactions on Circuits and Systems for Video Technology 25 , 9 (2014), 1480 -- 1494 . Ke Gu, Guangtao Zhai, Xiaokang Yang, Wenjun Zhang, and Chang Wen Chen. 2014. Automatic contrast enhancement technology with saliency preservation. IEEE Transactions on Circuits and Systems for Video Technology 25, 9 (2014), 1480--1494.","journal-title":"IEEE Transactions on Circuits and Systems for Video Technology"},{"key":"e_1_3_2_2_18_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2017.322"},{"key":"e_1_3_2_2_19_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.90"},{"key":"e_1_3_2_2_20_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.proeng.2011.08.412"},{"key":"e_1_3_2_2_21_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-01246-5_32"},{"key":"e_1_3_2_2_22_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-540-72849-8_60"},{"key":"e_1_3_2_2_23_1","volume-title":"Bayesian segnet: Model uncertainty in deep convolutional encoder-decoder architectures for scene understanding. arXiv preprint arXiv:1511.02680","author":"Kendall Alex","year":"2015","unstructured":"Alex Kendall , Vijay Badrinarayanan , and Roberto Cipolla . 2015. Bayesian segnet: Model uncertainty in deep convolutional encoder-decoder architectures for scene understanding. arXiv preprint arXiv:1511.02680 ( 2015 ). Alex Kendall, Vijay Badrinarayanan, and Roberto Cipolla. 2015. Bayesian segnet: Model uncertainty in deep convolutional encoder-decoder architectures for scene understanding. arXiv preprint arXiv:1511.02680 (2015)."},{"key":"e_1_3_2_2_24_1","volume-title":"What uncertainties do we need in bayesian deep learning for computer vision? Advances in neural information processing systems 30","author":"Kendall Alex","year":"2017","unstructured":"Alex Kendall and Yarin Gal . 2017. What uncertainties do we need in bayesian deep learning for computer vision? Advances in neural information processing systems 30 ( 2017 ). Alex Kendall and Yarin Gal. 2017. What uncertainties do we need in bayesian deep learning for computer vision? Advances in neural information processing systems 30 (2017)."},{"key":"e_1_3_2_2_25_1","volume-title":"Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980","author":"Kingma Diederik P","year":"2014","unstructured":"Diederik P Kingma and Jimmy Ba . 2014 . Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 (2014). Diederik P Kingma and Jimmy Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 (2014)."},{"key":"e_1_3_2_2_26_1","volume-title":"Efficient inference in fully connected crfs with gaussian edge potentials. Advances in neural information processing systems 24","author":"Kr\u00e4henb\u00fchl Philipp","year":"2011","unstructured":"Philipp Kr\u00e4henb\u00fchl and Vladlen Koltun . 2011. Efficient inference in fully connected crfs with gaussian edge potentials. Advances in neural information processing systems 24 ( 2011 ). Philipp Kr\u00e4henb\u00fchl and Vladlen Koltun. 2011. Efficient inference in fully connected crfs with gaussian edge potentials. Advances in neural information processing systems 24 (2011)."},{"key":"e_1_3_2_2_27_1","volume-title":"Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25","author":"Krizhevsky Alex","year":"2012","unstructured":"Alex Krizhevsky , Ilya Sutskever , and Geoffrey E Hinton . 2012. Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 ( 2012 ). Alex Krizhevsky, Ilya Sutskever, and Geoffrey E Hinton. 2012. Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012)."},{"key":"e_1_3_2_2_28_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.csda.2019.106816"},{"key":"e_1_3_2_2_29_1","doi-asserted-by":"publisher","DOI":"10.1037\/0097-7403.22.2.139"},{"key":"e_1_3_2_2_30_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.cviu.2019.04.006"},{"key":"e_1_3_2_2_31_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2017.106"},{"key":"e_1_3_2_2_32_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00326"},{"key":"e_1_3_2_2_33_1","volume-title":"Parsenet: Looking wider to see better. arXiv preprint arXiv:1506.04579","author":"Liu Wei","year":"2015","unstructured":"Wei Liu , Andrew Rabinovich , and Alexander C Berg . 2015 . Parsenet: Looking wider to see better. arXiv preprint arXiv:1506.04579 (2015). Wei Liu, Andrew Rabinovich, and Alexander C Berg. 2015. Parsenet: Looking wider to see better. arXiv preprint arXiv:1506.04579 (2015)."},{"key":"e_1_3_2_2_34_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR46437.2021.01142"},{"key":"e_1_3_2_2_35_1","unstructured":"Mingcan Ma12 Changqun Xia and Jia Li123. 2021. Pyramidal feature shrinking for salient object detection. (2021).  Mingcan Ma12 Changqun Xia and Jia Li123. 2021. Pyramidal feature shrinking for salient object detection. (2021)."},{"key":"e_1_3_2_2_36_1","volume-title":"A simple baseline for bayesian uncertainty in deep learning. Advances in Neural Information Processing Systems 32","author":"Maddox Wesley J","year":"2019","unstructured":"Wesley J Maddox , Pavel Izmailov , Timur Garipov , Dmitry P Vetrov , and Andrew Gordon Wilson . 2019. A simple baseline for bayesian uncertainty in deep learning. Advances in Neural Information Processing Systems 32 ( 2019 ). Wesley J Maddox, Pavel Izmailov, Timur Garipov, Dmitry P Vetrov, and Andrew Gordon Wilson. 2019. A simple baseline for bayesian uncertainty in deep learning. Advances in Neural Information Processing Systems 32 (2019)."},{"key":"e_1_3_2_2_37_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2014.39"},{"key":"e_1_3_2_2_38_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR46437.2021.00866"},{"key":"e_1_3_2_2_39_1","doi-asserted-by":"publisher","DOI":"10.5539\/mas.v5n4p152"},{"key":"e_1_3_2_2_40_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR42600.2020.00943"},{"key":"e_1_3_2_2_41_1","volume-title":"Pytorch: An imperative style, high-performance deep learning library. Advances in neural information processing systems 32","author":"Paszke Adam","year":"2019","unstructured":"Adam Paszke , Sam Gross , Francisco Massa , Adam Lerer , James Bradbury , Gregory Chanan , Trevor Killeen , Zeming Lin , Natalia Gimelshein , Luca Antiga , 2019 . Pytorch: An imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019). Adam Paszke, Sam Gross, Francisco Massa, Adam Lerer, James Bradbury, Gregory Chanan, Trevor Killeen, Zeming Lin, Natalia Gimelshein, Luca Antiga, et al. 2019. Pytorch: An imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019)."},{"key":"e_1_3_2_2_42_1","doi-asserted-by":"publisher","DOI":"10.1073\/pnas.1213775110"},{"key":"e_1_3_2_2_43_1","volume-title":"Cognitive processes in animal behavior","author":"Riley Donald A","unstructured":"Donald A Riley and HL Roitblat . 2018. Selective attention and related cognitive processes in pigeons . In Cognitive processes in animal behavior . Routledge , 249--276. Donald A Riley and HL Roitblat. 2018. Selective attention and related cognitive processes in pigeons. In Cognitive processes in animal behavior. Routledge, 249--276."},{"key":"e_1_3_2_2_44_1","volume-title":"6th International Conference on Learning Representations, ICLR 2018-Conference Track Proceedings","volume":"6","author":"Ritter Hippolyt","year":"2018","unstructured":"Hippolyt Ritter , Aleksandar Botev , and David Barber . 2018 . A scalable laplace approximation for neural networks . In 6th International Conference on Learning Representations, ICLR 2018-Conference Track Proceedings , Vol. 6 . International Conference on Representation Learning. Hippolyt Ritter, Aleksandar Botev, and David Barber. 2018. A scalable laplace approximation for neural networks. In 6th International Conference on Learning Representations, ICLR 2018-Conference Track Proceedings, Vol. 6. International Conference on Representation Learning."},{"key":"e_1_3_2_2_45_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICETET.2008.232"},{"key":"e_1_3_2_2_46_1","volume-title":"Evidential deep learning to quantify classification uncertainty. Advances in Neural Information Processing Systems 31","author":"Sensoy Murat","year":"2018","unstructured":"Murat Sensoy , Lance Kaplan , and Melih Kandemir . 2018. Evidential deep learning to quantify classification uncertainty. Advances in Neural Information Processing Systems 31 ( 2018 ). Murat Sensoy, Lance Kaplan, and Melih Kandemir. 2018. Evidential deep learning to quantify classification uncertainty. Advances in Neural Information Processing Systems 31 (2018)."},{"key":"e_1_3_2_2_47_1","volume-title":"Animal camouflage analysis: Chameleon database. Unpublished manuscript 2, 6","author":"Skurowski Przemyslaw","year":"2018","unstructured":"Przemyslaw Skurowski , Hassan Abdulameer , J Blaszczyk , Tomasz Depta , Adam Kornacki , and P Koziel . 2018. Animal camouflage analysis: Chameleon database. Unpublished manuscript 2, 6 ( 2018 ), 7. Przemyslaw Skurowski, Hassan Abdulameer, J Blaszczyk, Tomasz Depta, Adam Kornacki, and P Koziel. 2018. Animal camouflage analysis: Chameleon database. Unpublished manuscript 2, 6 (2018), 7."},{"key":"e_1_3_2_2_48_1","doi-asserted-by":"publisher","DOI":"10.1098\/rstb.2008.0217"},{"key":"e_1_3_2_2_49_1","volume-title":"Context-aware Cross-level Fusion Network for Camouflaged Object Detection. arXiv preprint arXiv:2105.12555","author":"Sun Yujia","year":"2021","unstructured":"Yujia Sun , Geng Chen , Tao Zhou , Yi Zhang , and Nian Liu . 2021. Context-aware Cross-level Fusion Network for Camouflaged Object Detection. arXiv preprint arXiv:2105.12555 ( 2021 ). Yujia Sun, Geng Chen, Tao Zhou, Yi Zhang, and Nian Liu. 2021. Context-aware Cross-level Fusion Network for Camouflaged Object Detection. arXiv preprint arXiv:2105.12555 (2021)."},{"key":"e_1_3_2_2_50_1","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0073733"},{"key":"e_1_3_2_2_51_1","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v34i07.6892"},{"key":"e_1_3_2_2_52_1","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v34i07.6916"},{"key":"e_1_3_2_2_53_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-01234-2_1"},{"key":"e_1_3_2_2_54_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.00403"},{"key":"e_1_3_2_2_55_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV48922.2021.00411"},{"key":"e_1_3_2_2_56_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2019.00064"},{"key":"e_1_3_2_2_57_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR46437.2021.01280"},{"key":"e_1_3_2_2_58_1","doi-asserted-by":"publisher","DOI":"10.1145\/3474085.3475231"},{"key":"e_1_3_2_2_59_1","doi-asserted-by":"publisher","DOI":"10.1145\/3474085.3475494"},{"key":"e_1_3_2_2_60_1","volume-title":"Nima Tajbakhsh, and Jianming Liang.","author":"Zhou Zongwei","year":"2018","unstructured":"Zongwei Zhou , Md Mahfuzur Rahman Siddiquee , Nima Tajbakhsh, and Jianming Liang. 2018 . Unet : A nested u-net architecture for medical image segmentation. In Deep learning in medical image analysis and multimodal learning for clinical decision support. Springer , 3--11. Zongwei Zhou, Md Mahfuzur Rahman Siddiquee, Nima Tajbakhsh, and Jianming Liang. 2018. Unet: A nested u-net architecture for medical image segmentation. In Deep learning in medical image analysis and multimodal learning for clinical decision support. Springer, 3--11."},{"key":"e_1_3_2_2_61_1","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v35i4.16475"}],"event":{"name":"MM '22: The 30th ACM International Conference on Multimedia","location":"Lisboa Portugal","acronym":"MM '22","sponsor":["SIGMM ACM Special Interest Group on Multimedia"]},"container-title":["Proceedings of the 30th ACM International Conference on Multimedia"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3503161.3548178","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3503161.3548178","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,17]],"date-time":"2025-06-17T19:00:20Z","timestamp":1750186820000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3503161.3548178"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,10,10]]},"references-count":61,"alternative-id":["10.1145\/3503161.3548178","10.1145\/3503161"],"URL":"https:\/\/doi.org\/10.1145\/3503161.3548178","relation":{},"subject":[],"published":{"date-parts":[[2022,10,10]]},"assertion":[{"value":"2022-10-10","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}