[{"page":"14069-14080","file_date_updated":"2024-01-08T10:09:14Z","issue":"12","publication":"IEEE Transactions on Pattern Analysis and Machine Intelligence","type":"journal_article","day":"01","status":"public","intvolume":"        45","department":[{"_id":"HeEd"}],"has_accepted_license":"1","date_created":"2024-01-08T09:59:46Z","file":[{"checksum":"465c28ef0b151b4b1fb47977ed5581ab","date_created":"2024-01-08T10:09:14Z","file_size":2370988,"file_name":"2023_IEEEToP_Ali.pdf","access_level":"open_access","date_updated":"2024-01-08T10:09:14Z","success":1,"creator":"dernst","file_id":"14740","relation":"main_file","content_type":"application/pdf"}],"date_published":"2023-12-01T00:00:00Z","article_type":"original","month":"12","language":[{"iso":"eng"}],"publisher":"IEEE","date_updated":"2024-01-08T10:11:46Z","oa":1,"volume":45,"article_processing_charge":"Yes (in subscription journal)","acknowledgement":"The work of Maria-Jose Jimenez, Eduardo Paluzo-Hidalgo and Manuel Soriano-Trigueros was supported in part by the Spanish grant Ministerio de Ciencia e Innovacion under Grants TED2021-129438B-I00 and PID2019-107339GB-I00, and in part by REXASI-PRO H-EU project, call HORIZON-CL4-2021-HUMAN-01-01 under Grant 101070028. The work of\r\nMaria-Jose Jimenez was supported by a grant of Convocatoria de la Universidad de Sevilla para la recualificacion del sistema universitario español, 2021-23, funded by the European Union, NextGenerationEU. The work of Vidit Nanda was supported in part by EPSRC under Grant EP/R018472/1 and in part by US AFOSR under Grant FA9550-22-1-0462. \r\nWe are grateful to the team of GUDHI and TEASPOON developers, for their work and their support. We are also grateful to Streamlit for providing extra resources to deploy the web app\r\nonline on Streamlit community cloud. We thank the anonymous referees for their helpful suggestions.","user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","oa_version":"Published Version","quality_controlled":"1","_id":"14739","publication_identifier":{"issn":["0162-8828"],"eissn":["1939-3539"]},"publication_status":"published","citation":{"ama":"Ali D, Asaad A, Jimenez M-J, Nanda V, Paluzo-Hidalgo E, Soriano Trigueros M. A survey of vectorization methods in topological data analysis. <i>IEEE Transactions on Pattern Analysis and Machine Intelligence</i>. 2023;45(12):14069-14080. doi:<a href=\"https://doi.org/10.1109/tpami.2023.3308391\">10.1109/tpami.2023.3308391</a>","mla":"Ali, Dashti, et al. “A Survey of Vectorization Methods in Topological Data Analysis.” <i>IEEE Transactions on Pattern Analysis and Machine Intelligence</i>, vol. 45, no. 12, IEEE, 2023, pp. 14069–80, doi:<a href=\"https://doi.org/10.1109/tpami.2023.3308391\">10.1109/tpami.2023.3308391</a>.","short":"D. Ali, A. Asaad, M.-J. Jimenez, V. Nanda, E. Paluzo-Hidalgo, M. Soriano Trigueros, IEEE Transactions on Pattern Analysis and Machine Intelligence 45 (2023) 14069–14080.","ista":"Ali D, Asaad A, Jimenez M-J, Nanda V, Paluzo-Hidalgo E, Soriano Trigueros M. 2023. A survey of vectorization methods in topological data analysis. IEEE Transactions on Pattern Analysis and Machine Intelligence. 45(12), 14069–14080.","apa":"Ali, D., Asaad, A., Jimenez, M.-J., Nanda, V., Paluzo-Hidalgo, E., &#38; Soriano Trigueros, M. (2023). A survey of vectorization methods in topological data analysis. <i>IEEE Transactions on Pattern Analysis and Machine Intelligence</i>. IEEE. <a href=\"https://doi.org/10.1109/tpami.2023.3308391\">https://doi.org/10.1109/tpami.2023.3308391</a>","ieee":"D. Ali, A. Asaad, M.-J. Jimenez, V. Nanda, E. Paluzo-Hidalgo, and M. Soriano Trigueros, “A survey of vectorization methods in topological data analysis,” <i>IEEE Transactions on Pattern Analysis and Machine Intelligence</i>, vol. 45, no. 12. IEEE, pp. 14069–14080, 2023.","chicago":"Ali, Dashti, Aras Asaad, Maria-Jose Jimenez, Vidit Nanda, Eduardo Paluzo-Hidalgo, and Manuel Soriano Trigueros. “A Survey of Vectorization Methods in Topological Data Analysis.” <i>IEEE Transactions on Pattern Analysis and Machine Intelligence</i>. IEEE, 2023. <a href=\"https://doi.org/10.1109/tpami.2023.3308391\">https://doi.org/10.1109/tpami.2023.3308391</a>."},"author":[{"first_name":"Dashti","full_name":"Ali, Dashti","last_name":"Ali"},{"full_name":"Asaad, Aras","last_name":"Asaad","first_name":"Aras"},{"first_name":"Maria-Jose","last_name":"Jimenez","full_name":"Jimenez, Maria-Jose"},{"first_name":"Vidit","full_name":"Nanda, Vidit","last_name":"Nanda"},{"last_name":"Paluzo-Hidalgo","full_name":"Paluzo-Hidalgo, Eduardo","first_name":"Eduardo"},{"id":"15ebd7cf-15bf-11ee-aebd-bb4bb5121ea8","full_name":"Soriano Trigueros, Manuel","last_name":"Soriano Trigueros","orcid":"0000-0003-2449-1433","first_name":"Manuel"}],"keyword":["Applied Mathematics","Artificial Intelligence","Computational Theory and Mathematics","Computer Vision and Pattern Recognition","Software"],"abstract":[{"text":"Attempts to incorporate topological information in supervised learning tasks have resulted in the creation of several techniques for vectorizing persistent homology barcodes. In this paper, we study thirteen such methods. Besides describing an organizational framework for these methods, we comprehensively benchmark them against three well-known classification tasks. Surprisingly, we discover that the best-performing method is a simple vectorization, which consists only of a few elementary summary statistics. Finally, we provide a convenient web application which has been designed to facilitate exploration and experimentation with various vectorization methods.","lang":"eng"}],"tmp":{"image":"/images/cc_by.png","legal_code_url":"https://creativecommons.org/licenses/by/4.0/legalcode","name":"Creative Commons Attribution 4.0 International Public License (CC-BY 4.0)","short":"CC BY (4.0)"},"ddc":["000"],"doi":"10.1109/tpami.2023.3308391","year":"2023","title":"A survey of vectorization methods in topological data analysis"},{"year":"2019","doi":"10.1109/tpami.2018.2857768","title":"Zero-shot learning - A comprehensive evaluation of the good, the bad and the ugly","external_id":{"isi":["000480343900015"],"arxiv":["1707.00600"]},"main_file_link":[{"open_access":"1","url":"https://arxiv.org/abs/1707.00600"}],"isi":1,"publication_status":"published","citation":{"chicago":"Xian, Yongqin, Christoph Lampert, Bernt Schiele, and Zeynep Akata. “Zero-Shot Learning - A Comprehensive Evaluation of the Good, the Bad and the Ugly.” <i>IEEE Transactions on Pattern Analysis and Machine Intelligence</i>. Institute of Electrical and Electronics Engineers (IEEE), 2019. <a href=\"https://doi.org/10.1109/tpami.2018.2857768\">https://doi.org/10.1109/tpami.2018.2857768</a>.","ieee":"Y. Xian, C. Lampert, B. Schiele, and Z. Akata, “Zero-shot learning - A comprehensive evaluation of the good, the bad and the ugly,” <i>IEEE Transactions on Pattern Analysis and Machine Intelligence</i>, vol. 41, no. 9. Institute of Electrical and Electronics Engineers (IEEE), pp. 2251–2265, 2019.","apa":"Xian, Y., Lampert, C., Schiele, B., &#38; Akata, Z. (2019). Zero-shot learning - A comprehensive evaluation of the good, the bad and the ugly. <i>IEEE Transactions on Pattern Analysis and Machine Intelligence</i>. Institute of Electrical and Electronics Engineers (IEEE). <a href=\"https://doi.org/10.1109/tpami.2018.2857768\">https://doi.org/10.1109/tpami.2018.2857768</a>","short":"Y. Xian, C. Lampert, B. Schiele, Z. Akata, IEEE Transactions on Pattern Analysis and Machine Intelligence 41 (2019) 2251–2265.","ista":"Xian Y, Lampert C, Schiele B, Akata Z. 2019. Zero-shot learning - A comprehensive evaluation of the good, the bad and the ugly. IEEE Transactions on Pattern Analysis and Machine Intelligence. 41(9), 2251–2265.","mla":"Xian, Yongqin, et al. “Zero-Shot Learning - A Comprehensive Evaluation of the Good, the Bad and the Ugly.” <i>IEEE Transactions on Pattern Analysis and Machine Intelligence</i>, vol. 41, no. 9, Institute of Electrical and Electronics Engineers (IEEE), 2019, pp. 2251–65, doi:<a href=\"https://doi.org/10.1109/tpami.2018.2857768\">10.1109/tpami.2018.2857768</a>.","ama":"Xian Y, Lampert C, Schiele B, Akata Z. Zero-shot learning - A comprehensive evaluation of the good, the bad and the ugly. <i>IEEE Transactions on Pattern Analysis and Machine Intelligence</i>. 2019;41(9):2251-2265. doi:<a href=\"https://doi.org/10.1109/tpami.2018.2857768\">10.1109/tpami.2018.2857768</a>"},"abstract":[{"lang":"eng","text":"Due to the importance of zero-shot learning, i.e. classifying images where there is a lack of labeled training data, the number of proposed approaches has recently increased steadily. We argue that it is time to take a step back and to analyze the status quo of the area. The purpose of this paper is three-fold. First, given the fact that there is no agreed upon zero-shot learning benchmark, we first define a new benchmark by unifying both the evaluation protocols and data splits of publicly available datasets used for this task. This is an important contribution as published results are often not comparable and sometimes even flawed due to, e.g. pre-training on zero-shot test classes. Moreover, we propose a new zero-shot learning dataset, the Animals with Attributes 2 (AWA2) dataset which we make publicly available both in terms of image features and the images themselves. Second, we compare and analyze a significant number of the state-of-the-art methods in depth, both in the classic zero-shot setting but also in the more realistic generalized zero-shot setting. Finally, we discuss in detail the limitations of the current status of the area which can be taken as a basis for advancing it."}],"author":[{"last_name":"Xian","full_name":"Xian, Yongqin","first_name":"Yongqin"},{"last_name":"Lampert","full_name":"Lampert, Christoph","orcid":"0000-0002-4561-241X","first_name":"Christoph","id":"40C20FD2-F248-11E8-B48F-1D18A9856A87"},{"full_name":"Schiele, Bernt","last_name":"Schiele","first_name":"Bernt"},{"first_name":"Zeynep","last_name":"Akata","full_name":"Akata, Zeynep"}],"arxiv":1,"volume":41,"oa":1,"date_updated":"2023-09-05T13:18:09Z","article_processing_charge":"No","_id":"6554","publication_identifier":{"issn":["0162-8828"],"eissn":["1939-3539"]},"user_id":"c635000d-4b10-11ee-a964-aac5a93f6ac1","oa_version":"Preprint","quality_controlled":"1","month":"09","date_published":"2019-09-01T00:00:00Z","article_type":"original","publisher":"Institute of Electrical and Electronics Engineers (IEEE)","scopus_import":"1","language":[{"iso":"eng"}],"department":[{"_id":"ChLa"}],"date_created":"2019-06-11T14:05:59Z","day":"01","type":"journal_article","intvolume":"        41","status":"public","issue":"9","publication":"IEEE Transactions on Pattern Analysis and Machine Intelligence","page":"2251 - 2265"}]