{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,13]],"date-time":"2025-12-13T06:57:06Z","timestamp":1765609026910},"reference-count":68,"publisher":"Springer Science and Business Media LLC","issue":"2","license":[{"start":{"date-parts":[[2023,8,31]],"date-time":"2023-08-31T00:00:00Z","timestamp":1693440000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,8,31]],"date-time":"2023-08-31T00:00:00Z","timestamp":1693440000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Int J Multimed Info Retr"],"published-print":{"date-parts":[[2023,12]]},"DOI":"10.1007\/s13735-023-00299-0","type":"journal-article","created":{"date-parts":[[2023,8,31]],"date-time":"2023-08-31T16:07:23Z","timestamp":1693498043000},"update-policy":"http:\/\/dx.doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Ornament image retrieval using few-shot learning"],"prefix":"10.1007","volume":"12","author":[{"given":"Sk Maidul","family":"Islam","sequence":"first","affiliation":[]},{"given":"Subhankar","family":"Joardar","sequence":"additional","affiliation":[]},{"given":"Arif Ahmed","family":"Sekh","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2023,8,31]]},"reference":[{"key":"299_CR1","doi-asserted-by":"crossref","unstructured":"Gajic B, Baldrich R (2018) Cross-domain fashion image retrieval. In: Proceedings of the IEEE conference on computer vision and pattern recognition workshops, pp 1869\u20131871","DOI":"10.1109\/CVPRW.2018.00243"},{"issue":"6","key":"299_CR2","doi-asserted-by":"publisher","first-page":"1175","DOI":"10.1109\/TMM.2016.2542983","volume":"18","author":"X Liang","year":"2016","unstructured":"Liang X, Lin L, Yang W, Luo P, Huang J, Yan S (2016) Clothes co-parsing via joint image segmentation and labeling with application to clothing retrieval. IEEE Trans Multimedia 18(6):1175\u20131186","journal-title":"IEEE Trans Multimedia"},{"key":"299_CR3","doi-asserted-by":"crossref","unstructured":"Lang Y, He Y, Yang F, Dong J, Xue H (2020) Which is plagiarism: fashion image retrieval based on regional representation for design protection. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition (CVPR)","DOI":"10.1109\/CVPR42600.2020.00267"},{"key":"299_CR4","doi-asserted-by":"crossref","unstructured":"Su H, Wang P, Liu L, Li H, Li Z, Zhang Y (2020) Where to look and how to describe: fashion image retrieval with an attentional heterogeneous bilinear network. In: IEEE transactions on circuits and systems for video technology","DOI":"10.1109\/TCSVT.2020.3034981"},{"key":"299_CR5","doi-asserted-by":"crossref","unstructured":"Corbiere C, Ben-Younes H, Ram\u00e9 A, Ollion C (2017) Leveraging weakly annotated data for fashion image retrieval and label prediction. In: Proceedings of the IEEE international conference on computer vision workshops, pp 2268\u20132274","DOI":"10.1109\/ICCVW.2017.266"},{"key":"299_CR6","doi-asserted-by":"crossref","unstructured":"Kang W-C, Fang C, Wang Z, McAuley J (2017) Visually-aware fashion recommendation and design with generative image models. In: 2017 IEEE international conference on data mining (ICDM). IEEE, pp 207\u2013216","DOI":"10.1109\/ICDM.2017.30"},{"key":"299_CR7","doi-asserted-by":"crossref","unstructured":"Yin R, Li K, Lu J, Zhang G (2019) Enhancing fashion recommendation with visual compatibility relationship. In: The world wide web conference, pp 3434\u20133440","DOI":"10.1145\/3308558.3313739"},{"key":"299_CR8","doi-asserted-by":"crossref","unstructured":"Hidayati SC, Hsu C-C, Chang Y-T, Hua K-L, Fu J, Cheng W-H (2018) What dress fits me best? fashion recommendation on the clothing style for personal body shape. In: Proceedings of the 26th ACM international conference on multimedia, pp 438\u2013446","DOI":"10.1145\/3240508.3240546"},{"key":"299_CR9","doi-asserted-by":"crossref","unstructured":"Verma S, Anand S, Arora C, Rai A (2018) Diversity in fashion recommendation using semantic parsing. In: 2018 25th IEEE international conference on image processing (ICIP). IEEE, pp 500\u2013504","DOI":"10.1109\/ICIP.2018.8451164"},{"key":"299_CR10","doi-asserted-by":"crossref","unstructured":"Khurana T, Mahajan K, Arora C, Rai A (2018) Exploiting texture cues for clothing parsing in fashion images. In: 2018 25th IEEE international conference on image processing (ICIP). IEEE, pp 2102\u20132106","DOI":"10.1109\/ICIP.2018.8451281"},{"key":"299_CR11","doi-asserted-by":"crossref","unstructured":"Dong H, Liang X, Zhang Y, Zhang X, Shen X, Xie Z, Wu B, Yin J (2020) Fashion editing with adversarial parsing learning. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, pp 8120\u20138128","DOI":"10.1109\/CVPR42600.2020.00814"},{"key":"299_CR12","doi-asserted-by":"crossref","unstructured":"Liu Z, Luo P, Qiu S, Wang X, Tang X (2016a) Deepfashion: powering robust clothes recognition and retrieval with rich annotations. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 1096\u20131104","DOI":"10.1109\/CVPR.2016.124"},{"key":"299_CR13","doi-asserted-by":"publisher","first-page":"112","DOI":"10.1016\/j.jvcir.2019.03.003","volume":"61","author":"W Zhou","year":"2019","unstructured":"Zhou W, Mok PY, Zhou Y, Zhou Y, Shen J, Qu Q, Chau KP (2019) Fashion recommendations through cross-media information retrieval. J Visual Commun Image Represent 61:112\u2013120","journal-title":"J Visual Commun Image Represent"},{"key":"299_CR14","doi-asserted-by":"crossref","unstructured":"Ge Y, Zhang R, Wang X, Tang X, Luo P (2019) Deepfashion2: a versatile benchmark for detection, pose estimation, segmentation and re-identification of clothing images. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, pp 5337\u20135345","DOI":"10.1109\/CVPR.2019.00548"},{"key":"299_CR15","doi-asserted-by":"crossref","unstructured":"Maidul Islam Sk, Joardar S, Sekh AAR (2021a) A large volume earring dataset for fashion image retrieval. In: Computer vision and image processing: 5th international conference, CVIP 2020, Prayagraj, India, December 4\u20136, 2020, Revised Selected Papers, Part II. Springer, pp 100\u2013111","DOI":"10.1007\/978-981-16-1092-9_9"},{"key":"299_CR16","doi-asserted-by":"crossref","unstructured":"Maidul Islam SK, Joardar S, Ahmed Sekh A (2023) Necklacefir: a large volume benchmarked necklace dataset for fashion image retrieval. In: Artificial intelligence: first international symposium, ISAI 2022, Haldia, India, February 17\u201322, 2022, Revised Selected Papers. Springer, pp 180\u2013190","DOI":"10.1007\/978-3-031-22485-0_17"},{"issue":"6","key":"299_CR17","doi-asserted-by":"publisher","first-page":"84","DOI":"10.1145\/3065386","volume":"60","author":"A Krizhevsky","year":"2017","unstructured":"Krizhevsky A, Sutskever I, Hinton GE (2017) Imagenet classification with deep convolutional neural networks. Commun ACM 60(6):84\u201390","journal-title":"Commun ACM"},{"key":"299_CR18","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"},{"key":"299_CR19","doi-asserted-by":"crossref","unstructured":"Long J, Shelhamer E, Darrell T (2015) Fully convolutional networks for semantic segmentation. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 3431\u20133440","DOI":"10.1109\/CVPR.2015.7298965"},{"issue":"9","key":"299_CR20","doi-asserted-by":"publisher","first-page":"2070","DOI":"10.1109\/TPAMI.2018.2852750","volume":"41","author":"Z Li","year":"2018","unstructured":"Li Z, Tang J, Mei T (2018) Deep collaborative embedding for social image understanding. IEEE Trans Pattern Anal Mach Intell 41(9):2070\u20132083","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"299_CR21","unstructured":"Finn C, Abbeel P, Levine S (2017) Model-agnostic meta-learning for fast adaptation of deep networks. In: International conference on machine learning. PMLR, pp 1126\u20131135"},{"key":"299_CR22","unstructured":"Vinyals O, Blundell C, Lillicrap T, Wierstra D, et\u00a0al (2016) Matching networks for one shot learning. In: Advances in neural information processing systems, vol 29"},{"key":"299_CR23","doi-asserted-by":"crossref","unstructured":"Qiao S, Liu C, Shen W, Yuille AL (2018) Few-shot image recognition by predicting parameters from activations. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 7229\u20137238","DOI":"10.1109\/CVPR.2018.00755"},{"key":"299_CR24","unstructured":"Snell J, Swersky K, Zemel R (2017) Prototypical networks for few-shot learning. In: Advances in neural information processing systems, vol 30"},{"key":"299_CR25","doi-asserted-by":"publisher","first-page":"2387","DOI":"10.1109\/JSTARS.2021.3052869","volume":"14","author":"X Sun","year":"2021","unstructured":"Sun X, Wang B, Wang Z, Li H, Li H, Kun F (2021) Research progress on few-shot learning for remote sensing image interpretation. IEEE J Select Top Appl Earth Observ Remote Sens 14:2387\u20132402","journal-title":"IEEE J Select Top Appl Earth Observ Remote Sens"},{"key":"299_CR26","first-page":"11054","volume":"34","author":"E Perez","year":"2021","unstructured":"Perez E, Kiela D, Cho K (2021) True few-shot learning with language models. Adv Neural Inf Process Syst 34:11054\u201311070","journal-title":"Adv Neural Inf Process Syst"},{"key":"299_CR27","unstructured":"Alayrac J-B, Donahue J, Luc P, Miech A, Barr I, Hasson Y, Lenc K, Mensch A, Millican K, Reynolds M, et\u00a0al (2022) Flamingo: a visual language model for few-shot learning. arXiv:2204.14198"},{"key":"299_CR28","doi-asserted-by":"crossref","unstructured":"Yang L, Li L, Zhang Z, Zhou X, Zhou E, Liu Y (2020) Dpgn: distribution propagation graph network for few-shot learning. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, pp 13390\u201313399","DOI":"10.1109\/CVPR42600.2020.01340"},{"key":"299_CR29","doi-asserted-by":"publisher","DOI":"10.1016\/j.compag.2020.105542","volume":"175","author":"D Arg\u00fceso","year":"2020","unstructured":"Arg\u00fceso D, Picon A, Irusta U, Medela A, San-Emeterio MG, Bereciartua A, Alvarez-Gila A (2020) Few-shot learning approach for plant disease classification using images taken in the field. Comput Electron Agric 175:105542","journal-title":"Comput Electron Agric"},{"key":"299_CR30","doi-asserted-by":"crossref","unstructured":"Huang J, Feris RS, Chen Q, Yan S (2015) Cross-domain image retrieval with a dual attribute-aware ranking network. In: Proceedings of the IEEE international conference on computer vision, pp 1062\u20131070","DOI":"10.1109\/ICCV.2015.127"},{"issue":"4","key":"299_CR31","first-page":"1","volume":"2","author":"SK Maidul Islam","year":"2021","unstructured":"Maidul Islam SK, Joardar S, Dogra DP, Ahmed Sekh A (2021) Ornament image retrieval using multimodal fusion. SN Comput Sci 2(4):1\u20139","journal-title":"SN Comput Sci"},{"key":"299_CR32","doi-asserted-by":"crossref","unstructured":"Sun G-L, Wu X, Chen H-H, Peng Q (2015) Clothing style recognition using fashion attribute detection. In: Proceedings of the 8th international conference on mobile multimedia communications, pp 145\u2013148","DOI":"10.4108\/icst.mobimedia.2015.259089"},{"key":"299_CR33","doi-asserted-by":"crossref","unstructured":"Liu Z, Yan S, Luo P, Wang X, Tang X (2016b) Fashion landmark detection in the wild. In: European conference on computer vision. Springer, pp 229\u2013245","DOI":"10.1007\/978-3-319-46475-6_15"},{"key":"299_CR34","unstructured":"Kenan\u00a0EA, Kassim AA, Lim JH, Tham JY (2018a) Learning attribute representations with localization for flexible fashion search. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 7708\u20137717"},{"key":"299_CR35","doi-asserted-by":"publisher","first-page":"212","DOI":"10.1016\/j.patrec.2018.07.019","volume":"112","author":"EA Kenan","year":"2018","unstructured":"Kenan EA, Lim JH, Tham JY, Kassim AA (2018) Which shirt for my first date? towards a flexible attribute-based fashion query system. Pattern Recogn Lett 112:212\u2013218","journal-title":"Pattern Recogn Lett"},{"key":"299_CR36","doi-asserted-by":"crossref","unstructured":"Wang W, Xu Y, Shen J, Zhu S-C (2018) Attentive fashion grammar network for fashion landmark detection and clothing category classification. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 4271\u20134280","DOI":"10.1109\/CVPR.2018.00449"},{"key":"299_CR37","doi-asserted-by":"crossref","unstructured":"Jaradat S (2017) Deep cross-domain fashion recommendation. In: Proceedings of the eleventh ACM conference on recommender systems, pp 407\u2013410","DOI":"10.1145\/3109859.3109861"},{"issue":"5","key":"299_CR38","doi-asserted-by":"publisher","DOI":"10.1016\/j.ipm.2020.102276","volume":"57","author":"G Xiaoling","year":"2020","unstructured":"Xiaoling G, Gao F, Tan M, Peng P (2020) Fashion analysis and understanding with artificial intelligence. Inf Process Manag 57(5):102276","journal-title":"Inf Process Manag"},{"key":"299_CR39","unstructured":"Shi M, Van\u00a0DL (2020) Using artificial intelligence to analyze fashion trends. arXiv:2005.00986"},{"issue":"2","key":"299_CR40","doi-asserted-by":"publisher","first-page":"87","DOI":"10.1177\/0887302X18821187","volume":"37","author":"L Zhao","year":"2019","unstructured":"Zhao L, Min C (2019) The rise of fashion informatics: a case of data-mining-based social network analysis in fashion. Cloth Text Res J 37(2):87\u2013102","journal-title":"Cloth Text Res J"},{"key":"299_CR41","doi-asserted-by":"crossref","unstructured":"Zhu S, Urtasun R, Fidler S, Lin D, Loy CC (2017) Be your own prada: fashion synthesis with structural coherence. In: Proceedings of the IEEE international conference on computer vision, pp 1680\u20131688","DOI":"10.1109\/ICCV.2017.186"},{"key":"299_CR42","doi-asserted-by":"publisher","first-page":"356","DOI":"10.1016\/j.neucom.2020.07.092","volume":"414","author":"N Pandey","year":"2020","unstructured":"Pandey N, Savakis A (2020) Poly-gan: multi-conditioned gan for fashion synthesis. Neurocomputing 414:356\u2013364","journal-title":"Neurocomputing"},{"key":"299_CR43","unstructured":"Kenan\u00a0EA, Lim JH, Tham JY, Kassim A (2019) Semantically consistent hierarchical text to fashion image synthesis with an enhanced-attentional generative adversarial network. In: 2019 IEEE\/CVF international conference on computer vision workshop (ICCVW). IEEE, pp 3121\u20133124"},{"key":"299_CR44","doi-asserted-by":"crossref","unstructured":"Loni B, Menendez M, Georgescu M, Galli L, Massari C, Altingovde IS, Martinenghi D, Melenhorst M, Vliegendhart R, Larson M (2013) Fashion-focused creative commons social dataset. In: Proceedings of the 4th ACM multimedia systems conference, pp 72\u201377","DOI":"10.1145\/2483977.2483984"},{"key":"299_CR45","doi-asserted-by":"crossref","unstructured":"Loni B, Cheung LY, Riegler M, Bozzon A, Gottlieb L, Larson M (2014) Fashion 10000: an enriched social image dataset for fashion and clothing. In: Proceedings of the 5th acm multimedia systems conference, pp 41\u201346","DOI":"10.1145\/2557642.2563675"},{"key":"299_CR46","doi-asserted-by":"crossref","unstructured":"Huang J, Xia W, Yan S (2014) Deep search with attribute-aware deep network. In: Proceedings of the 22nd ACM international conference on Multimedia, pp 731\u2013732","DOI":"10.1145\/2647868.2654885"},{"key":"299_CR47","doi-asserted-by":"crossref","unstructured":"Kiapour MH, Han X, Lazebnik S, Berg AC, Berg TL (2015) Where to buy it: matching street clothing photos in online shops. In: Proceedings of the IEEE international conference on computer vision, pp 3343\u20133351","DOI":"10.1109\/ICCV.2015.382"},{"key":"299_CR48","doi-asserted-by":"crossref","unstructured":"Liu K-H, Chen T-Y, Chen C-S (2016c) Mvc: a dataset for view-invariant clothing retrieval and attribute prediction. In: Proceedings of the 2016 ACM on international conference on multimedia retrieval, pp 313\u2013316","DOI":"10.1145\/2911996.2912058"},{"key":"299_CR49","doi-asserted-by":"crossref","unstructured":"Kuang Z, Gao Y, Li G, Luo P, Chen Y, Lin L, Zhang W (2019) Fashion retrieval via graph reasoning networks on a similarity pyramid. In: Proceedings of the IEEE\/CVF international conference on computer vision, pp 3066\u20133075","DOI":"10.1109\/ICCV.2019.00316"},{"key":"299_CR50","first-page":"11741","volume":"34","author":"Z Ma","year":"2020","unstructured":"Ma Z, Dong J, Long Z, Zhang Y, He Y, Xue H, Ji S (2020) Fine-grained fashion similarity learning by attribute-specific embedding network. Proc AAAI Conf Artif Intell 34:11741\u201311748","journal-title":"Proc AAAI Conf Artif Intell"},{"key":"299_CR51","doi-asserted-by":"crossref","unstructured":"Zou X, Kong X, Wong W, Wang C, Liu Y, Cao Y (2019) Fashionai: a hierarchical dataset for fashion understanding. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition workshops","DOI":"10.1109\/CVPRW.2019.00039"},{"key":"299_CR52","unstructured":"Xiao H, Rasul K, Vollgraf R (2017) Fashion-mnist: a novel image dataset for benchmarking machine learning algorithms. arXiv:1708.07747"},{"key":"299_CR53","doi-asserted-by":"crossref","unstructured":"Cheng Z-Q, Wu X, Liu Y, Hua X-S (2017) Video2shop: exact matching clothes in videos to online shopping images. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 4048\u20134056","DOI":"10.1109\/CVPR.2017.444"},{"key":"299_CR54","unstructured":"Rostamzadeh N, Hosseini S, Boquet T, Stokowiec W, Zhang Y, Jauvin C, Pal C (2018) Fashion-gen: the generative fashion dataset and challenge. arXiv:1806.08317"},{"key":"299_CR55","doi-asserted-by":"crossref","unstructured":"Wu H, Gao Y, Guo X, Al-Halah Z, Rennie S, Grauman K, Feris R (2021) Fashion iq: a new dataset towards retrieving images by natural language feedback. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, pp 11307\u201311317","DOI":"10.1109\/CVPR46437.2021.01115"},{"key":"299_CR56","doi-asserted-by":"crossref","unstructured":"Song Y, Li Y, Wu B, Chen C-Y, Zhang X, Adam H (2017) Learning unified embedding for apparel recognition. In: Proceedings of the IEEE international conference on computer vision workshops, pp 2243\u20132246","DOI":"10.1109\/ICCVW.2017.262"},{"key":"299_CR57","doi-asserted-by":"crossref","unstructured":"Zhang Y, Pan P, Zheng Y, Zhao K, Zhang Y, Ren X, Jin R (2018) Visual search at alibaba. In: Proceedings of the 24th ACM SIGKDD international conference on knowledge discovery and data mining, pp 993\u20131001","DOI":"10.1145\/3219819.3219820"},{"key":"299_CR58","doi-asserted-by":"crossref","unstructured":"Jiang S, Wu Y, Fu Y (2016) Deep bi-directional cross-triplet embedding for cross-domain clothing retrieval. In: Proceedings of the 24th ACM international conference on multimedia, pp 52\u201356","DOI":"10.1145\/2964284.2967182"},{"key":"299_CR59","doi-asserted-by":"crossref","unstructured":"Kinli F, Ozcan B, Kirac F (2019) Fashion image retrieval with capsule networks. In: Proceedings of the IEEE\/CVF international conference on computer vision workshops","DOI":"10.1109\/ICCVW.2019.00376"},{"key":"299_CR60","doi-asserted-by":"crossref","unstructured":"Lin K, Yang H-F, Liu K-H, Hsiao J-H, Chen C-S (2015) Rapid clothing retrieval via deep learning of binary codes and hierarchical search. In: Proceedings of the 5th ACM on international conference on multimedia retrieval, pp 499\u2013502","DOI":"10.1145\/2671188.2749318"},{"key":"299_CR61","doi-asserted-by":"crossref","unstructured":"D\u2019Innocente A, Garg N, Zhang Y, Bazzani L, Donoser M (2021) Localized triplet loss for fine-grained fashion image retrieval. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, pp 3910\u20133915","DOI":"10.1109\/CVPRW53098.2021.00435"},{"key":"299_CR62","doi-asserted-by":"publisher","first-page":"142669","DOI":"10.1109\/ACCESS.2020.3013631","volume":"8","author":"Y Miao","year":"2020","unstructured":"Miao Y, Li G, Bao C, Zhang J, Wang J (2020) Clothingnet: cross-domain clothing retrieval with feature fusion and quadruplet loss. IEEE Access 8:142669\u2013142679","journal-title":"IEEE Access"},{"key":"299_CR63","unstructured":"Kucer M, Murray N (2016) A detect-then-retrieve model for multi-domain fashion item retrieval. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition workshops"},{"key":"299_CR64","doi-asserted-by":"crossref","unstructured":"Wang Z, Gu Y, Zhang Y, Zhou J, Gu X (2017) Clothing retrieval with visual attention model. In: 2017 IEEE visual communications and image processing (VCIP). IEEE, pp 1\u20134","DOI":"10.1109\/VCIP.2017.8305144"},{"issue":"9","key":"299_CR65","doi-asserted-by":"publisher","first-page":"4519","DOI":"10.1007\/s00521-018-3691-y","volume":"32","author":"H Zhang","year":"2020","unstructured":"Zhang H, Sun Y, Liu L, Wang X, Li L, Liu W (2020) Clothingout: a category-supervised gan model for clothing segmentation and retrieval. Neural Comput Appl 32(9):4519\u20134530","journal-title":"Neural Comput Appl"},{"key":"299_CR66","doi-asserted-by":"crossref","unstructured":"Maidul Islam SK, Joardar S, Sekh AA (2022) Dssn: dual shallow siamese network for fashion image retrieval. Multimedia Tools Appl 1\u201317","DOI":"10.1007\/s11042-022-14204-0"},{"key":"299_CR67","unstructured":"Lake B, Salakhutdinov R, Gross J, Tenenbaum J (2011) One shot learning of simple visual concepts. In: Proceedings of the annual meeting of the cognitive science society, vol\u00a033"},{"key":"299_CR68","doi-asserted-by":"crossref","unstructured":"Deng J, Dong W, Socher R, Li L-J, Li K, Fei-Fei L (2009) Imagenet: a large-scale hierarchical image database. In: 2009 IEEE conference on computer vision and pattern recognition. IEEE, pp 248\u2013255","DOI":"10.1109\/CVPR.2009.5206848"}],"container-title":["International Journal of Multimedia Information Retrieval"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s13735-023-00299-0.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s13735-023-00299-0\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s13735-023-00299-0.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,12,2]],"date-time":"2023-12-02T14:16:55Z","timestamp":1701526615000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s13735-023-00299-0"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,8,31]]},"references-count":68,"journal-issue":{"issue":"2","published-print":{"date-parts":[[2023,12]]}},"alternative-id":["299"],"URL":"https:\/\/doi.org\/10.1007\/s13735-023-00299-0","relation":{},"ISSN":["2192-6611","2192-662X"],"issn-type":[{"value":"2192-6611","type":"print"},{"value":"2192-662X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,8,31]]},"assertion":[{"value":"27 March 2023","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"11 June 2023","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"6 August 2023","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"31 August 2023","order":4,"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 there is no conflict of interest regarding the publication of this article.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}},{"value":"This article does not contain any studies with human participants or animals performed by any of the authors.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethical approval"}},{"value":"Informed consent was obtained from all individual participants included in the study.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Informed consent"}}],"article-number":"30"}}