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Appl."],"published-print":{"date-parts":[[2024,1,31]]},"abstract":"<jats:p>\n                    Automatically classifying plant leaves is a challenging fine-grained classification task because of the diversity in leaf morphology, including size, texture, shape, and venation. Although powerful deep learning-based methods have achieved great improvement in leaf classification, these methods still require a large number of well-labeled samples for supervised training, which is difficult to get. In contrast, relying on the specific coarse-to-fine classification strategy, human botanists only require a small number of samples for accurate leaf recognition. Inspired by the classification strategy of human botanists, we propose a novel\n                    <jats:italic>\n                      S\n                      <jats:sup>2<\/jats:sup>\n                      CL-Leaf Net\n                    <\/jats:italic>\n                    , which exploits multi-granularity clues with a hierarchical attention mechanism and boosts the learning ability with the supervised sampling contrastive learning with limited training samples to classify plant leaves as human botanists do. Specifically, to fully explore and exploit the subtle details of the leaves, a novel sampling transformation mechanism is combined with the supervised contrastive learning to enhance the network\u2019s perception of details by amplifying the discriminative regions with a weighted sampling of different regions. Furthermore, we construct the hierarchical attention mechanism to produce attention maps of different granularity, which helps to discover details in leaves that are important for classification. Experiments are conducted on the open-access leaf datasets, including Flavia, Swedish, and LeafSnap, which prove the effectiveness of the proposed\n                    <jats:italic>\n                      S\n                      <jats:sup>2<\/jats:sup>\n                      CL-Leaf Net\n                    <\/jats:italic>\n                    .\n                  <\/jats:p>","DOI":"10.1145\/3615659","type":"journal-article","created":{"date-parts":[[2023,8,15]],"date-time":"2023-08-15T05:44:44Z","timestamp":1692078284000},"page":"1-20","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":8,"title":["<i>\n                      S\n                      <sup>2<\/sup>\n                      CL-Leaf Net\n                    <\/i>\n                    : Recognizing Leaf Images Like Human Botanists"],"prefix":"10.1145","volume":"20","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-1901-5363","authenticated-orcid":false,"given":"Cong","family":"Zou","sequence":"first","affiliation":[{"name":"State Key Laboratory of Information Security, Institute of Information Engineering, Chinese Academy of Sciences; School of Cyber Security, University of Chinese Academy of Sciences, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4792-1945","authenticated-orcid":false,"given":"Rui","family":"Wang","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Information Security, Institute of Information Engineering, Chinese Academy of Sciences; School of Cyber Security, University of Chinese Academy of Sciences, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3063-1957","authenticated-orcid":false,"given":"Cheng","family":"Jin","sequence":"additional","affiliation":[{"name":"School of Computer Science, Shanghai Key Laboratory of Intelligent Information Processing, Fudan University, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3786-2299","authenticated-orcid":false,"given":"Sanyi","family":"Zhang","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Information Security, Institute of Information Engineering, Chinese Academy of Sciences, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0351-2939","authenticated-orcid":false,"given":"Xin","family":"Wang","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Technology, Tsinghua University, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2023,9,18]]},"reference":[{"key":"e_1_3_1_2_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.knosys.2010.11.002"},{"key":"e_1_3_1_3_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.biosystemseng.2015.08.003"},{"issue":"3","key":"e_1_3_1_4_2","doi-asserted-by":"crossref","first-page":"1997","DOI":"10.3233\/JIFS-169911","article-title":"A study on plant recognition using conventional image processing and deep learning approaches","volume":"36","author":"Pearline S. 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