{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,19]],"date-time":"2026-06-19T06:50:14Z","timestamp":1781851814534,"version":"3.54.5"},"reference-count":5,"publisher":"Wiley","license":[{"start":{"date-parts":[[2019,4,3]],"date-time":"2019-04-03T00:00:00Z","timestamp":1554249600000},"content-version":"unspecified","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["41876098"],"award-info":[{"award-number":["41876098"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["JCYJ20151117173236192"],"award-info":[{"award-number":["JCYJ20151117173236192"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Shenzhen Science and Technology Project","award":["41876098"],"award-info":[{"award-number":["41876098"]}]},{"name":"Shenzhen Science and Technology Project","award":["JCYJ20151117173236192"],"award-info":[{"award-number":["JCYJ20151117173236192"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Mobile Information Systems"],"published-print":{"date-parts":[[2019,4,3]]},"abstract":"<jats:p>This paper is devoted to a lightweight convolutional neural network based on the attention mechanism called the tiny attention network (TANet). The TANet consists of three main parts termed as a reduction module, self-attention operation, and group convolution. The reduction module alleviates information loss caused by the pooling operation. The new parameter-free self-attention operation makes the model to focus on learning important parts of images. The group convolution achieves model compression and multibranch fusion. Using the main parts, the proposed network enables efficient plankton classification on mobile devices. The performance of the proposed network is evaluated on the Plankton dataset collected by Oregon State University\u2019s Hatfield Marine Science Center. The results show that TANet outperforms other deep models in speed (31.8\u2009ms per image), size (648\u2009kB, the size of the hard disk space occupied by the model), and accuracy (Top-1 76.5%, Top-5 96.3%).<\/jats:p>","DOI":"10.1155\/2019\/6536925","type":"journal-article","created":{"date-parts":[[2019,4,3]],"date-time":"2019-04-03T19:34:31Z","timestamp":1554320071000},"page":"1-8","source":"Crossref","is-referenced-by-count":5,"title":["TANet: A Tiny Plankton Classification Network for Mobile Devices"],"prefix":"10.1155","volume":"2019","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-0403-1923","authenticated-orcid":true,"given":"Xiu","family":"Li","sequence":"first","affiliation":[{"name":"Department of Automation, Graduate School at Shenzhen, Tsinghua University, Shenzhen, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1330-3193","authenticated-orcid":true,"given":"Rujiao","family":"Long","sequence":"additional","affiliation":[{"name":"Department of Automation, Graduate School at Shenzhen, Tsinghua University, Shenzhen, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0767-1726","authenticated-orcid":true,"given":"Jiangpeng","family":"Yan","sequence":"additional","affiliation":[{"name":"Department of Automation, Graduate School at Shenzhen, Tsinghua University, Shenzhen, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7113-4212","authenticated-orcid":true,"given":"Kun","family":"Jin","sequence":"additional","affiliation":[{"name":"Department of Automation, Graduate School at Shenzhen, Tsinghua University, Shenzhen, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4884-5876","authenticated-orcid":true,"given":"Jihae","family":"Lee","sequence":"additional","affiliation":[{"name":"Institute for Interdisciplinary Information Sciences, Tsinghua University, Beijing, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","reference":[{"key":"1","doi-asserted-by":"publisher","DOI":"10.1109\/joe.2004.836995"},{"key":"2","doi-asserted-by":"publisher","DOI":"10.1109\/joe.2013.2280035"},{"key":"3","doi-asserted-by":"publisher","DOI":"10.1145\/3065386"},{"key":"6","doi-asserted-by":"publisher","DOI":"10.1109\/tpami.2016.2577031"},{"issue":"4","key":"14","first-page":"3","volume":"56","year":"2015","journal-title":"Fiber"}],"container-title":["Mobile Information Systems"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/downloads.hindawi.com\/journals\/misy\/2019\/6536925.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/downloads.hindawi.com\/journals\/misy\/2019\/6536925.xml","content-type":"application\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/downloads.hindawi.com\/journals\/misy\/2019\/6536925.pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2019,4,3]],"date-time":"2019-04-03T19:34:35Z","timestamp":1554320075000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.hindawi.com\/journals\/misy\/2019\/6536925\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019,4,3]]},"references-count":5,"alternative-id":["6536925","6536925"],"URL":"https:\/\/doi.org\/10.1155\/2019\/6536925","relation":{},"ISSN":["1574-017X","1875-905X"],"issn-type":[{"value":"1574-017X","type":"print"},{"value":"1875-905X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2019,4,3]]}}}