{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,11]],"date-time":"2026-04-11T13:05:00Z","timestamp":1775912700730,"version":"3.50.1"},"publisher-location":"New York, NY, USA","reference-count":48,"publisher":"ACM","license":[{"start":{"date-parts":[[2023,10,21]],"date-time":"2023-10-21T00:00:00Z","timestamp":1697846400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61972013?61932007"],"award-info":[{"award-number":["61972013?61932007"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2023,10,21]]},"DOI":"10.1145\/3583780.3614787","type":"proceedings-article","created":{"date-parts":[[2023,10,21]],"date-time":"2023-10-21T07:45:42Z","timestamp":1697874342000},"page":"1228-1237","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":3,"title":["AutoMRM: A Model Retrieval Method Based on Multimodal Query and Meta-learning"],"prefix":"10.1145","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-5572-4378","authenticated-orcid":false,"given":"Zhaotian","family":"Li","sequence":"first","affiliation":[{"name":"Beihang University, Beijing, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0828-5544","authenticated-orcid":false,"given":"Binhang","family":"Qi","sequence":"additional","affiliation":[{"name":"Beihang University, Beijing, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7654-5574","authenticated-orcid":false,"given":"Hailong","family":"Sun","sequence":"additional","affiliation":[{"name":"Beihang University, Beijing, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9895-4600","authenticated-orcid":false,"given":"Xiang","family":"Gao","sequence":"additional","affiliation":[{"name":"Beihang University, Beijing, China"}]}],"member":"320","published-online":{"date-parts":[[2023,10,21]]},"reference":[{"key":"e_1_3_2_1_1_1","doi-asserted-by":"publisher","DOI":"10.1007\/3-540-45372-5_32"},{"key":"e_1_3_2_1_2_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.artint.2016.04.003"},{"key":"e_1_3_2_1_3_1","doi-asserted-by":"publisher","DOI":"10.1023\/A:1021713901879"},{"key":"e_1_3_2_1_4_1","doi-asserted-by":"publisher","DOI":"10.1145\/3357384.3357896"},{"key":"e_1_3_2_1_5_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2021.115053"},{"key":"e_1_3_2_1_6_1","unstructured":"Alexey Dosovitskiy Lucas Beyer Alexander Kolesnikov Dirk Weissenborn Xiaohua Zhai Thomas Unterthiner Mostafa Dehghani Matthias Minderer Georg Heigold Sylvain Gelly etal 2020. An image is worth 16x16 words: Transformers for image recognition at scale. arXiv preprint arXiv:2010.11929 (2020).  Alexey Dosovitskiy Lucas Beyer Alexander Kolesnikov Dirk Weissenborn Xiaohua Zhai Thomas Unterthiner Mostafa Dehghani Matthias Minderer Georg Heigold Sylvain Gelly et al. 2020. An image is worth 16x16 words: Transformers for image recognition at scale. arXiv preprint arXiv:2010.11929 (2020)."},{"key":"e_1_3_2_1_7_1","doi-asserted-by":"publisher","DOI":"10.1007\/3-540-60428-6_16"},{"key":"e_1_3_2_1_8_1","volume-title":"Proceedings of the ICML-2005 Workshop on Meta-learning. 12--19","author":"Giraud-Carrier Christophe","year":"2005","unstructured":"Christophe Giraud-Carrier and Foster Provost . 2005 . Toward a justification of meta-learning: Is the no free lunch theorem a show-stopper . In Proceedings of the ICML-2005 Workshop on Meta-learning. 12--19 . Christophe Giraud-Carrier and Foster Provost. 2005. Toward a justification of meta-learning: Is the no free lunch theorem a show-stopper. In Proceedings of the ICML-2005 Workshop on Meta-learning. 12--19."},{"key":"e_1_3_2_1_9_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.90"},{"key":"e_1_3_2_1_10_1","volume-title":"Julia Anna Bingler, and Markus Leippold","author":"Hershcovich Daniel","year":"2022","unstructured":"Daniel Hershcovich , Nicolas Webersinke , Mathias Kraus , Julia Anna Bingler, and Markus Leippold . 2022 . Towards climate awareness in NLP research. arXiv preprint arXiv:2205.05071 (2022). Daniel Hershcovich, Nicolas Webersinke, Mathias Kraus, Julia Anna Bingler, and Markus Leippold. 2022. Towards climate awareness in NLP research. arXiv preprint arXiv:2205.05071 (2022)."},{"key":"e_1_3_2_1_11_1","volume-title":"Complexity measures of supervised classification problems","author":"Ho Tin Kam","year":"2002","unstructured":"Tin Kam Ho and Mitra Basu . 2002. Complexity measures of supervised classification problems . IEEE transactions on pattern analysis and machine intelligence, Vol. 24 , 3 ( 2002 ), 289--300. Tin Kam Ho and Mitra Basu. 2002. Complexity measures of supervised classification problems. IEEE transactions on pattern analysis and machine intelligence, Vol. 24, 3 (2002), 289--300."},{"key":"e_1_3_2_1_12_1","volume-title":"Mobilenets: Efficient convolutional neural networks for mobile vision applications. arXiv preprint arXiv:1704.04861","author":"Howard Andrew G","year":"2017","unstructured":"Andrew G Howard , Menglong Zhu , Bo Chen , Dmitry Kalenichenko , Weijun Wang , Tobias Weyand , Marco Andreetto , and Hartwig Adam . 2017 . Mobilenets: Efficient convolutional neural networks for mobile vision applications. arXiv preprint arXiv:1704.04861 (2017). Andrew G Howard, Menglong Zhu, Bo Chen, Dmitry Kalenichenko, Weijun Wang, Tobias Weyand, Marco Andreetto, and Hartwig Adam. 2017. Mobilenets: Efficient convolutional neural networks for mobile vision applications. arXiv preprint arXiv:1704.04861 (2017)."},{"key":"e_1_3_2_1_13_1","unstructured":"Qiang Hu Yuejun Guo Maxime Cordy Xiaofei Xie Mike Papadakis and Yves Le Traon. 2023. LaF: Labeling-Free Model Selection for Automated Deep Neural Network Reusing. arxiv: 2204.03994 [cs.LG]  Qiang Hu Yuejun Guo Maxime Cordy Xiaofei Xie Mike Papadakis and Yves Le Traon. 2023. LaF: Labeling-Free Model Selection for Automated Deep Neural Network Reusing. arxiv: 2204.03994 [cs.LG]"},{"key":"e_1_3_2_1_14_1","volume-title":"SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and 0.5 MB model size. arXiv preprint arXiv:1602.07360","author":"Iandola Forrest N","year":"2016","unstructured":"Forrest N Iandola , Song Han , Matthew W Moskewicz , Khalid Ashraf , William J Dally , and Kurt Keutzer . 2016. SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and 0.5 MB model size. arXiv preprint arXiv:1602.07360 ( 2016 ). Forrest N Iandola, Song Han, Matthew W Moskewicz, Khalid Ashraf, William J Dally, and Kurt Keutzer. 2016. SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and 0.5 MB model size. arXiv preprint arXiv:1602.07360 (2016)."},{"key":"e_1_3_2_1_15_1","doi-asserted-by":"publisher","DOI":"10.1145\/3243734.3243757"},{"key":"e_1_3_2_1_16_1","volume-title":"ExploreKit: Automatic Feature Generation and Selection. In 2016 IEEE 16th International Conference on Data Mining (ICDM). 979--984","author":"Katz Gilad","year":"2016","unstructured":"Gilad Katz , Eui Chul Richard Shin , and Dawn Song . 2016 . ExploreKit: Automatic Feature Generation and Selection. In 2016 IEEE 16th International Conference on Data Mining (ICDM). 979--984 . https:\/\/doi.org\/10.1109\/ICDM.2016.0123 10.1109\/ICDM.2016.0123 Gilad Katz, Eui Chul Richard Shin, and Dawn Song. 2016. ExploreKit: Automatic Feature Generation and Selection. In 2016 IEEE 16th International Conference on Data Mining (ICDM). 979--984. https:\/\/doi.org\/10.1109\/ICDM.2016.0123"},{"key":"e_1_3_2_1_17_1","volume-title":"Automated algorithm selection: Survey and perspectives. Evolutionary computation","author":"Kerschke Pascal","year":"2019","unstructured":"Pascal Kerschke , Holger H Hoos , Frank Neumann , and Heike Trautmann . 2019. Automated algorithm selection: Survey and perspectives. Evolutionary computation , Vol. 27 , 1 ( 2019 ), 3--45. Pascal Kerschke, Holger H Hoos, Frank Neumann, and Heike Trautmann. 2019. Automated algorithm selection: Survey and perspectives. Evolutionary computation, Vol. 27, 1 (2019), 3--45."},{"key":"e_1_3_2_1_18_1","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2020.2964726"},{"key":"e_1_3_2_1_19_1","unstructured":"Alex Krizhevsky Geoffrey Hinton etal 2009. Learning multiple layers of features from tiny images. (2009).  Alex Krizhevsky Geoffrey Hinton et al. 2009. Learning multiple layers of features from tiny images. (2009)."},{"key":"e_1_3_2_1_20_1","doi-asserted-by":"publisher","DOI":"10.1145\/3065386"},{"key":"e_1_3_2_1_21_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.artint.2018.10.004"},{"key":"e_1_3_2_1_22_1","doi-asserted-by":"publisher","DOI":"10.1145\/3347711"},{"key":"e_1_3_2_1_23_1","volume-title":"Content-Based Search for Deep Generative Models. arXiv preprint arXiv:2210.03116","author":"Lu Daohan","year":"2022","unstructured":"Daohan Lu , Sheng-Yu Wang , Nupur Kumari , Rohan Agarwal , David Bau , and Jun-Yan Zhu . 2022. Content-Based Search for Deep Generative Models. arXiv preprint arXiv:2210.03116 ( 2022 ). Daohan Lu, Sheng-Yu Wang, Nupur Kumari, Rohan Agarwal, David Bau, and Jun-Yan Zhu. 2022. Content-Based Search for Deep Generative Models. arXiv preprint arXiv:2210.03116 (2022)."},{"key":"e_1_3_2_1_24_1","doi-asserted-by":"publisher","DOI":"10.1007\/s10115-013-0700-4"},{"key":"e_1_3_2_1_25_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-01264-9_8"},{"key":"e_1_3_2_1_26_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICSE43902.2021.00045"},{"key":"e_1_3_2_1_27_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.ins.2022.01.040"},{"key":"e_1_3_2_1_28_1","volume-title":"What is a support vector machine? Nature biotechnology","author":"Noble William S","year":"2006","unstructured":"William S Noble . 2006. What is a support vector machine? Nature biotechnology , Vol. 24 , 12 ( 2006 ), 1565--1567. William S Noble. 2006. What is a support vector machine? Nature biotechnology, Vol. 24, 12 (2006), 1565--1567."},{"key":"e_1_3_2_1_29_1","doi-asserted-by":"publisher","DOI":"10.1007\/3-540-36182-0_14"},{"key":"e_1_3_2_1_30_1","unstructured":"Bernhard Pfahringer Hilan Bensusan and Christophe G Giraud-Carrier. 2000. Meta-Learning by Landmarking Various Learning Algorithms.. In ICML. Citeseer 743--750.  Bernhard Pfahringer Hilan Bensusan and Christophe G Giraud-Carrier. 2000. Meta-Learning by Landmarking Various Learning Algorithms.. In ICML. Citeseer 743--750."},{"key":"e_1_3_2_1_31_1","volume-title":"International conference on machine learning. PMLR, 8748--8763","author":"Radford Alec","year":"2021","unstructured":"Alec Radford , Jong Wook Kim , Chris Hallacy , Aditya Ramesh , Gabriel Goh , Sandhini Agarwal , Girish Sastry , Amanda Askell , Pamela Mishkin , Jack Clark , 2021 . Learning transferable visual models from natural language supervision . In International conference on machine learning. PMLR, 8748--8763 . Alec Radford, Jong Wook Kim, Chris Hallacy, Aditya Ramesh, Gabriel Goh, Sandhini Agarwal, Girish Sastry, Amanda Askell, Pamela Mishkin, Jack Clark, et al. 2021. Learning transferable visual models from natural language supervision. In International conference on machine learning. PMLR, 8748--8763."},{"key":"e_1_3_2_1_32_1","unstructured":"Alec Radford Jeffrey Wu Rewon Child David Luan Dario Amodei Ilya Sutskever etal 2019. Language models are unsupervised multitask learners. OpenAI blog Vol. 1 8 (2019) 9.  Alec Radford Jeffrey Wu Rewon Child David Luan Dario Amodei Ilya Sutskever et al. 2019. Language models are unsupervised multitask learners. OpenAI blog Vol. 1 8 (2019) 9."},{"key":"e_1_3_2_1_33_1","volume-title":"Advances in computers.","author":"Rice John R","unstructured":"John R Rice . 1976. The algorithm selection problem . In Advances in computers. Vol. 15 . Elsevier , 65--118. John R Rice. 1976. The algorithm selection problem. In Advances in computers. Vol. 15. Elsevier, 65--118."},{"key":"e_1_3_2_1_34_1","volume-title":"Carlos Soares, Joaquin Vanschoren, and Andr\u00e9 CPLF de Carvalho.","author":"Rivolli Adriano","year":"2018","unstructured":"Adriano Rivolli , Lu'is PF Garcia , Carlos Soares, Joaquin Vanschoren, and Andr\u00e9 CPLF de Carvalho. 2018 . Characterizing classification datasets: a study of meta-features for meta-learning. arXiv preprint arXiv:1808.10406 (2018). Adriano Rivolli, Lu'is PF Garcia, Carlos Soares, Joaquin Vanschoren, and Andr\u00e9 CPLF de Carvalho. 2018. Characterizing classification datasets: a study of meta-features for meta-learning. arXiv preprint arXiv:1808.10406 (2018)."},{"key":"e_1_3_2_1_35_1","doi-asserted-by":"publisher","DOI":"10.1002\/widm.1249"},{"key":"e_1_3_2_1_36_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00474"},{"key":"e_1_3_2_1_37_1","volume-title":"Hugginggpt: Solving ai tasks with chatgpt and its friends in huggingface. arXiv preprint arXiv:2303.17580","author":"Shen Yongliang","year":"2023","unstructured":"Yongliang Shen , Kaitao Song , Xu Tan , Dongsheng Li , Weiming Lu , and Yueting Zhuang . 2023 . Hugginggpt: Solving ai tasks with chatgpt and its friends in huggingface. arXiv preprint arXiv:2303.17580 (2023). Yongliang Shen, Kaitao Song, Xu Tan, Dongsheng Li, Weiming Lu, and Yueting Zhuang. 2023. Hugginggpt: Solving ai tasks with chatgpt and its friends in huggingface. arXiv preprint arXiv:2303.17580 (2023)."},{"key":"e_1_3_2_1_38_1","volume-title":"Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556","author":"Simonyan Karen","year":"2014","unstructured":"Karen Simonyan and Andrew Zisserman . 2014. Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556 ( 2014 ). Karen Simonyan and Andrew Zisserman. 2014. Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556 (2014)."},{"key":"e_1_3_2_1_39_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2015.7298594"},{"key":"e_1_3_2_1_40_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.00293"},{"key":"e_1_3_2_1_41_1","doi-asserted-by":"publisher","DOI":"10.1145\/3269206.3269299"},{"key":"e_1_3_2_1_42_1","doi-asserted-by":"publisher","DOI":"10.1007\/s10994-017-5686-9"},{"key":"e_1_3_2_1_43_1","volume-title":"Attention is all you need. Advances in neural information processing systems","author":"Vaswani Ashish","year":"2017","unstructured":"Ashish Vaswani , Noam Shazeer , Niki Parmar , Jakob Uszkoreit , Llion Jones , Aidan N Gomez , \u0141ukasz Kaiser , and Illia Polosukhin . 2017. Attention is all you need. Advances in neural information processing systems , Vol. 30 ( 2017 ). Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, \u0141ukasz Kaiser, and Illia Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems, Vol. 30 (2017)."},{"key":"e_1_3_2_1_44_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICME.2017.8019347"},{"key":"e_1_3_2_1_45_1","volume-title":"Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing: System Demonstrations. Association for Computational Linguistics, Online, 38--45","author":"Wolf Thomas","year":"2020","unstructured":"Thomas Wolf , Lysandre Debut , Victor Sanh , Julien Chaumond , Clement Delangue , Anthony Moi , Pierric Cistac , Tim Rault , R\u00e9mi Louf , Morgan Funtowicz , Joe Davison , Sam Shleifer , Patrick von Platen , Clara Ma , Yacine Jernite , Julien Plu , Canwen Xu , Teven Le Scao , Sylvain Gugger , Mariama Drame , Quentin Lhoest , and Alexander M. Rush . 2020. Transformers: State-of-the-Art Natural Language Processing . In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing: System Demonstrations. Association for Computational Linguistics, Online, 38--45 . https:\/\/www.aclweb.org\/anthology\/ 2020 .emnlp-demos.6 Thomas Wolf, Lysandre Debut, Victor Sanh, Julien Chaumond, Clement Delangue, Anthony Moi, Pierric Cistac, Tim Rault, R\u00e9mi Louf, Morgan Funtowicz, Joe Davison, Sam Shleifer, Patrick von Platen, Clara Ma, Yacine Jernite, Julien Plu, Canwen Xu, Teven Le Scao, Sylvain Gugger, Mariama Drame, Quentin Lhoest, and Alexander M. Rush. 2020. Transformers: State-of-the-Art Natural Language Processing. In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing: System Demonstrations. Association for Computational Linguistics, Online, 38--45. https:\/\/www.aclweb.org\/anthology\/2020.emnlp-demos.6"},{"key":"e_1_3_2_1_46_1","doi-asserted-by":"publisher","DOI":"10.1109\/4235.585893"},{"key":"e_1_3_2_1_47_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICMLA.2008.62"},{"key":"e_1_3_2_1_48_1","doi-asserted-by":"publisher","DOI":"10.1145\/3446776"}],"event":{"name":"CIKM '23: The 32nd ACM International Conference on Information and Knowledge Management","location":"Birmingham United Kingdom","acronym":"CIKM '23","sponsor":["SIGWEB ACM Special Interest Group on Hypertext, Hypermedia, and Web","SIGIR ACM Special Interest Group on Information Retrieval"]},"container-title":["Proceedings of the 32nd ACM International Conference on Information and Knowledge Management"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3583780.3614787","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3583780.3614787","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,17]],"date-time":"2025-06-17T16:36:56Z","timestamp":1750178216000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3583780.3614787"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,10,21]]},"references-count":48,"alternative-id":["10.1145\/3583780.3614787","10.1145\/3583780"],"URL":"https:\/\/doi.org\/10.1145\/3583780.3614787","relation":{},"subject":[],"published":{"date-parts":[[2023,10,21]]},"assertion":[{"value":"2023-10-21","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}