{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,21]],"date-time":"2026-01-21T05:00:12Z","timestamp":1768971612122,"version":"3.49.0"},"reference-count":54,"publisher":"Springer Science and Business Media LLC","issue":"22","license":[{"start":{"date-parts":[[2025,7,3]],"date-time":"2025-07-03T00:00:00Z","timestamp":1751500800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,7,3]],"date-time":"2025-07-03T00:00:00Z","timestamp":1751500800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"DOI":"10.13039\/501100015501","name":"Qu\u1ef9 \u0110\u1ed5i m\u1edbi s\u00e1ng t\u1ea1o Vingroup","doi-asserted-by":"publisher","award":["VINIF.2019.DA19"],"award-info":[{"award-number":["VINIF.2019.DA19"]}],"id":[{"id":"10.13039\/501100015501","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000001","name":"National Science Foundation","doi-asserted-by":"publisher","award":["NSF#2025234"],"award-info":[{"award-number":["NSF#2025234"]}],"id":[{"id":"10.13039\/100000001","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001691","name":"Japan Society for the Promotion of Science","doi-asserted-by":"publisher","award":["JP16H06302, JP18H04120, JP21H04907, JP20K23355, JP21K18023"],"award-info":[{"award-number":["JP16H06302, JP18H04120, JP21H04907, JP20K23355, JP21K18023"]}],"id":[{"id":"10.13039\/501100001691","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001695","name":"Japan Science and Technology Corporation","doi-asserted-by":"publisher","award":["JPMJCR20D3, JPMJCR18A6"],"award-info":[{"award-number":["JPMJCR20D3, JPMJCR18A6"]}],"id":[{"id":"10.13039\/501100001695","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Neural Comput & Applic"],"published-print":{"date-parts":[[2025,8]]},"DOI":"10.1007\/s00521-025-11417-w","type":"journal-article","created":{"date-parts":[[2025,7,3]],"date-time":"2025-07-03T04:48:09Z","timestamp":1751518089000},"page":"18091-18108","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["GUNNEL: guided mixup augmentation and multi-model fusion for aquatic animal segmentation"],"prefix":"10.1007","volume":"37","author":[{"given":"Minh-Quan","family":"Le","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7363-2610","authenticated-orcid":false,"given":"Trung-Nghia","family":"Le","sequence":"additional","affiliation":[]},{"given":"Tam V.","family":"Nguyen","sequence":"additional","affiliation":[]},{"given":"Isao","family":"Echizen","sequence":"additional","affiliation":[]},{"given":"Minh-Triet","family":"Tran","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2025,7,3]]},"reference":[{"key":"11417_CR1","doi-asserted-by":"crossref","unstructured":"Le T-N, Sugimoto A, Ono S, Kawasaki H (2020) Attention r-cnn for accident detection. In: IEEE Intell Veh Symp","DOI":"10.1109\/IV47402.2020.9304730"},{"key":"11417_CR2","doi-asserted-by":"crossref","unstructured":"Cea MV, Diedrich K, Bakalo R, Ness L, Richmond D (2020) Multi-task learning for detection and classification of cancer in screening mammography. In: International Conference on medical image computing and computer-assisted intervention, pp 241\u2013250","DOI":"10.1007\/978-3-030-59725-2_24"},{"key":"11417_CR3","doi-asserted-by":"crossref","unstructured":"Vo DM, Le T-N, Sugimoto A (2018) Balancing content and style with two-stream fcns for style transfer. In: WACV","DOI":"10.1109\/WACV.2018.00152"},{"key":"11417_CR4","unstructured":"Nguyen V-T, Le T-N, Bui Q-M, Tran M-T, Duong AD (2012) Smart shopping assistant: A multimedia and social media augmented system with mobile devices to enhance customers\u2019 experience and interaction. In: Pacific Asia conference on information systems"},{"key":"11417_CR5","doi-asserted-by":"crossref","unstructured":"Tran M-T, Nguyen TV, Hoang T-H, Le T-N, Nguyen K-T, Dinh D-T, Nguyen T-A, Nguyen H-D, Nguyen T-T, Hoang X-N, Vo-Ho V-K, Do T-L, Nguyen L, Le M-Q, Nguyen-Dinh H-P, Pham T-T, Nguyen X-V, Nguyen E-R, Tran Q-C, Tran H, Dao H, Tran M-K, Nguyen Q-T, Vu-Le T-A, Nguyen T-P, Diep G-H, Do MN (2020) itask\u2014intelligent traffic analysis software kit. In: CVPR Workshops","DOI":"10.1109\/CVPRW50498.2020.00314"},{"key":"11417_CR6","doi-asserted-by":"crossref","unstructured":"Nguyen K-D, Nguyen HH, Le T-N, Yamagishi J, Echizen I (2021) Effectiveness of detection-based and regression-based approaches for estimating mask-wearing ratio. In: IEEE international conference on automatic face and gesture recognition, pp 1\u20138","DOI":"10.1109\/FG52635.2021.9667046"},{"key":"11417_CR7","first-page":"45","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. CVIU 184:45\u201356","journal-title":"CVIU"},{"key":"11417_CR8","doi-asserted-by":"crossref","unstructured":"Liu L, Ouyang W, Wang X, Fieguth PW, Chen J, Liu X, Pietik\u00e4inen M (2019) Deep learning for generic object detection: a survey. IJCV, 128","DOI":"10.1007\/s11263-019-01247-4"},{"key":"11417_CR9","doi-asserted-by":"crossref","unstructured":"Minaee S, Boykov YY, Porikli F, Plaza AJ, Kehtarnavaz N, Terzopoulos D (2021) Image segmentation using deep learning: a survey. IEEE TPAMI","DOI":"10.1109\/TPAMI.2021.3059968"},{"key":"11417_CR10","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.conb.2019.10.008","volume":"60","author":"MW Mathis","year":"2020","unstructured":"Mathis MW, Mathis A (2020) Deep learning tools for the measurement of animal behavior in neuroscience. Current Opin Neurobiol 60:1\u201311","journal-title":"Current Opin Neurobiol"},{"key":"11417_CR11","doi-asserted-by":"crossref","unstructured":"Maekawa T, Ohara K, Zhang Y, Fukutomi M, Matsumoto S, Matsumura K, Shidara H, Yamazaki SJ, Fujisawa R, Ide K, Nagaya N, Yamazaki K, Koike S, Miyatake T, Kimura KD, Ogawa H, Takahashi S, Yoda K (2020) Deep learning-assisted comparative analysis of animal trajectories with deephl. Nat Commun, 11","DOI":"10.1038\/s41467-020-19105-0"},{"key":"11417_CR12","doi-asserted-by":"crossref","unstructured":"Parkhi OM, Vedaldi A, Zisserman A, Jawahar CV (2012) Cats and dogs. In: CVPR","DOI":"10.1109\/CVPR.2012.6248092"},{"key":"11417_CR13","unstructured":"Wah C, Branson S, Welinder P, Perona P, Belongie S (2011) The caltech-UCSD birds-200-2011 dataset. Technical Report CNS-TR-2011-001, California Institute of Technology"},{"key":"11417_CR14","doi-asserted-by":"crossref","unstructured":"Wu X, Zhan C, Lai Y, Cheng M-M, Yang J (2019) IP102: A large-scale benchmark dataset for insect pest recognition. In: CVPR","DOI":"10.1109\/CVPR.2019.00899"},{"key":"11417_CR15","doi-asserted-by":"crossref","unstructured":"He K, Gkioxari G, Doll\u00e1r P, Girshick R (2017) Mask R-CNN. In: ICCV","DOI":"10.1109\/ICCV.2017.322"},{"key":"11417_CR16","doi-asserted-by":"crossref","unstructured":"Cai Z, Vasconcelos N (2018) Cascade r-cnn: Delving into high quality object detection. In: CVPR","DOI":"10.1109\/CVPR.2018.00644"},{"key":"11417_CR17","doi-asserted-by":"crossref","unstructured":"Huang Z, Huang L, Gong Y, Huang C, Wang X (2019) Mask scoring R-CNN. In: CVPR","DOI":"10.1109\/CVPR.2019.00657"},{"key":"11417_CR18","unstructured":"Ren S, He K, Girshick R, Sun J (2015) Faster r-cnn: Towards real-time object detection with region proposal networks. In: NeurIPS"},{"key":"11417_CR19","doi-asserted-by":"crossref","unstructured":"Hafiz AM, Bhat GM (2020) A survey on instance segmentation: state of the art. IJMIR 9","DOI":"10.1007\/s13735-020-00195-x"},{"key":"11417_CR20","doi-asserted-by":"crossref","unstructured":"Wang J, Chen K, Xu R, Liu Z, Loy CC, Lin D (2019) Carafe: content-aware reassembly of features. In: ICCV","DOI":"10.1109\/ICCV.2019.00310"},{"key":"11417_CR21","doi-asserted-by":"crossref","unstructured":"Zhu X, Hu H, Lin S, Dai J (2019) Deformable convnets v2: More deformable, better results. CVPR","DOI":"10.1109\/CVPR.2019.00953"},{"key":"11417_CR22","doi-asserted-by":"crossref","unstructured":"Cao Y, Xu J, Lin S, Wei F, Hu H (2019) Gcnet: Non-local networks meet squeeze-excitation networks and beyond. In: ICCV Workshop","DOI":"10.1109\/ICCVW.2019.00246"},{"key":"11417_CR23","doi-asserted-by":"crossref","unstructured":"Kirillov A, Wu Y, He K, Girshick R (2020) Pointrend: Image segmentation as rendering. In: CVPR","DOI":"10.1109\/CVPR42600.2020.00982"},{"key":"11417_CR24","doi-asserted-by":"crossref","unstructured":"Rossi L, Karimi A, Prati A (2021) A novel region of interest extraction layer for instance segmentation. ICPR","DOI":"10.1109\/ICPR48806.2021.9412258"},{"key":"11417_CR25","doi-asserted-by":"crossref","unstructured":"Wu Y, He K (2018) Group normalization. In: ECCV","DOI":"10.1007\/978-3-030-01261-8_1"},{"key":"11417_CR26","unstructured":"Qiao S, Wang H, Liu C, Shen W, Yuille A (2019) Weight standardization. arXiv preprint arXiv:1903.10520"},{"key":"11417_CR27","unstructured":"Dalal N, Triggs B (2005) Histograms of oriented gradients for human detection. In: CVPR"},{"key":"11417_CR28","doi-asserted-by":"crossref","unstructured":"Girshick R, Donahue J, Darrell T, Malik J (2014) Rich feature hierarchies for accurate object detection and semantic segmentation. In: CVPR","DOI":"10.1109\/CVPR.2014.81"},{"key":"11417_CR29","doi-asserted-by":"crossref","unstructured":"Bodla N, Singh B, Chellappa R, Davis LS (2017) Soft-NMS\u2014improving object detection with one line of code. In: ICCV","DOI":"10.1109\/ICCV.2017.593"},{"key":"11417_CR30","doi-asserted-by":"publisher","first-page":"104117","DOI":"10.1016\/j.imavis.2021.104117","volume":"107","author":"R Solovyev","year":"2021","unstructured":"Solovyev R, Wang W, Gabruseva T (2021) Weighted boxes fusion: Ensembling boxes from different object detection models. Image Vis Comput 107:104117","journal-title":"Image Vis Comput"},{"key":"11417_CR31","doi-asserted-by":"crossref","unstructured":"Shorten C, Khoshgoftaar T (2019) A survey on image data augmentation for deep learning. J Big Data, 6","DOI":"10.1186\/s40537-019-0197-0"},{"key":"11417_CR32","doi-asserted-by":"crossref","unstructured":"Takahashi R, Matsubara T, Uehara K (2020) Data augmentation using random image cropping and patching for deep cnns. IEEE TCSVT, 30(9)","DOI":"10.1109\/TCSVT.2019.2935128"},{"key":"11417_CR33","doi-asserted-by":"crossref","unstructured":"Szegedy C, Liu W, Jia Y, Sermanet P, Reed SE, Anguelov D, Erhan D, Vanhoucke V, Rabinovich A (2015) Going deeper with convolutions. In: CVPR","DOI":"10.1109\/CVPR.2015.7298594"},{"key":"11417_CR34","unstructured":"Zhang H, Cisse M, Dauphin YN, Lopez-Paz D (2018) mixup: Beyond empirical risk minimization. In: ICLR"},{"key":"11417_CR35","unstructured":"Goodfellow IJ, Pouget-Abadie J, Mirza M, Xu B, Warde-Farley D, Ozair S, Courville A, Bengio Y (2014) Generative adversarial nets. In: NeurIPS, pp 2672\u20132680"},{"key":"11417_CR36","doi-asserted-by":"crossref","unstructured":"Yun S, Han D, Oh SJ, Chun S, Choe J, Yoo Y (2019) CutMix: Regularization strategy to train strong classifiers with localizable features. In: ICCV","DOI":"10.1109\/ICCV.2019.00612"},{"key":"11417_CR37","unstructured":"Bochkovskiy A, Wang C-Y, Liao H (2020) YOLOv4: Optimal speed and accuracy of object detection. ArXiv arXiv:2004.10934"},{"key":"11417_CR38","doi-asserted-by":"crossref","unstructured":"Fang H-S, Sun J, Wang R, Gou M, Li Y-L, Lu C (2019) Instaboost: Boosting instance segmentation via probability map guided copy-pasting. In: ICCV","DOI":"10.1109\/ICCV.2019.00077"},{"key":"11417_CR39","doi-asserted-by":"crossref","unstructured":"Ghiasi G, Cui Y, Srinivas A, Qian R, Lin T-Y, Cubuk ED, Le QV, Zoph B (2021) Simple copy-paste is a strong data augmentation method for instance segmentation. In: CVPR, pp 2918\u20132928","DOI":"10.1109\/CVPR46437.2021.00294"},{"key":"11417_CR40","doi-asserted-by":"crossref","unstructured":"Dvornik N, Mairal J, Schmid C (2018) Modeling visual context is key to augmenting object detection datasets. In: ECCV","DOI":"10.1007\/978-3-030-01258-8_23"},{"key":"11417_CR41","doi-asserted-by":"crossref","unstructured":"Dwibedi D, Misra I, Hebert M (2017) Cut, paste and learn: Surprisingly easy synthesis for instance detection. In: ICCV","DOI":"10.1109\/ICCV.2017.146"},{"key":"11417_CR42","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.neucom.2023.02.018","volume":"532","author":"N Wang","year":"2023","unstructured":"Wang N, Chen T, Liu S, Wang R, Karimi HR, Lin Y (2023) Deep learning-based visual detection of marine organisms: A survey. Neurocomputing 532:1\u201332","journal-title":"Neurocomputing"},{"issue":"2","key":"11417_CR43","doi-asserted-by":"publisher","first-page":"2434","DOI":"10.1007\/s10489-022-03622-0","volume":"53","author":"H Yu","year":"2023","unstructured":"Yu H, Li X, Feng Y, Han S (2023) Multiple attentional path aggregation network for marine object detection. Appl Intell 53(2):2434\u20132451","journal-title":"Appl Intell"},{"key":"11417_CR44","doi-asserted-by":"crossref","unstructured":"Cheng J, Wu Z, Wang S, Demonceaux C, Jiang Q (2023) Bidirectional collaborative mentoring network for marine organism detection and beyond. IEEE Trans Circuits Syst Video Technol","DOI":"10.1109\/TCSVT.2023.3264442"},{"key":"11417_CR45","doi-asserted-by":"crossref","unstructured":"Li L, Rigall E, Dong J, Chen G (2020) MAS3K: an open dataset for marine animal segmentation. In: BenchCouncil international symposium on benchmarking, measuring, and optimizing, pp 194\u2013212","DOI":"10.1007\/978-3-030-71058-3_12"},{"key":"11417_CR46","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: CVPR","DOI":"10.1109\/CVPR42600.2020.00285"},{"key":"11417_CR47","doi-asserted-by":"crossref","unstructured":"Le T-N, Cao Y, Nguyen T-C, Le M-Q, Nguyen K-D, Do T-T, Tran M-T, Nguyen TV (2021) Camouflaged instance segmentation in-the-wild: Dataset, method, and benchmark suite. IEEE TIP","DOI":"10.1109\/TIP.2021.3130490"},{"key":"11417_CR48","unstructured":"Jocher G, Nishimura K, Mineeva T, Vilari\u00f1o R (2020) Yolov5. Code repository https:\/\/github.com\/ultralytics\/yolov5"},{"key":"11417_CR49","doi-asserted-by":"crossref","unstructured":"Lin T-Y, Maire M, Belongie S, Hays J, Perona P, Ramanan D, Doll\u00e1r P, Zitnick CL (2014) Microsoft coco: common objects in context. In: ECCV","DOI":"10.1007\/978-3-319-10602-1_48"},{"key":"11417_CR50","doi-asserted-by":"crossref","unstructured":"Wang C-Y, Liao H-YM, Wu Y-H, Chen P-Y, Hsieh J-W, Yeh I-H (2020) Cspnet: A new backbone that can enhance learning capability of cnn. Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition workshops, pp 390\u2013391","DOI":"10.1109\/CVPRW50498.2020.00203"},{"key":"11417_CR51","doi-asserted-by":"crossref","unstructured":"Liu S, Qi L, Qin H, Shi J, Jia J (2018) Path aggregation network for instance segmentation. In: Proceedings of the IEEE conference on computer vision and pattern recognition (CVPR), pp 8759\u20138768","DOI":"10.1109\/CVPR.2018.00913"},{"key":"11417_CR52","doi-asserted-by":"crossref","unstructured":"Bottou L (2010) Large-scale machine learning with stochastic gradient descent. In: COMPSTAT","DOI":"10.1007\/978-3-7908-2604-3_16"},{"key":"11417_CR53","doi-asserted-by":"crossref","unstructured":"Li R, He C, Li S, Zhang Y, Zhang L (2023) Dynamask: dynamic mask selection for instance segmentation. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, pp 11279\u201311288","DOI":"10.1109\/CVPR52729.2023.01085"},{"key":"11417_CR54","doi-asserted-by":"crossref","unstructured":"Yin B, Zhang X, Fan D-P, Jiao S, Cheng M-M, Van\u00a0Gool L, Hou Q (2024) Camoformer: Masked separable attention for camouflaged object detection. IEEE Trans Pattern Anal Mach Intell","DOI":"10.1109\/TPAMI.2024.3438565"}],"container-title":["Neural Computing and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-025-11417-w.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s00521-025-11417-w\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-025-11417-w.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,9,7]],"date-time":"2025-09-07T00:47:38Z","timestamp":1757206058000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s00521-025-11417-w"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,7,3]]},"references-count":54,"journal-issue":{"issue":"22","published-print":{"date-parts":[[2025,8]]}},"alternative-id":["11417"],"URL":"https:\/\/doi.org\/10.1007\/s00521-025-11417-w","relation":{},"ISSN":["0941-0643","1433-3058"],"issn-type":[{"value":"0941-0643","type":"print"},{"value":"1433-3058","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,7,3]]},"assertion":[{"value":"10 August 2023","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"3 June 2025","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"3 July 2025","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of Interest"}}]}}