{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,30]],"date-time":"2026-07-30T14:18:57Z","timestamp":1785421137756,"version":"3.56.0"},"reference-count":43,"publisher":"Association for Computing Machinery (ACM)","issue":"1s","license":[{"start":{"date-parts":[[2021,1,31]],"date-time":"2021-01-31T00:00:00Z","timestamp":1612051200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"funder":[{"DOI":"10.13039\/501100012166","name":"National Key R&D Program of China","doi-asserted-by":"crossref","award":["2018AAA0100300"],"award-info":[{"award-number":["2018AAA0100300"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"crossref"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"crossref","award":["U1701267, 61772149, and 61936002"],"award-info":[{"award-number":["U1701267, 61772149, and 61936002"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"crossref"}]},{"name":"Guangxi Science and Technology Project","award":["ZY20198016, AB20238013, 2019GXNSFFA245014, AD18216004, and AD18281079"],"award-info":[{"award-number":["ZY20198016, AB20238013, 2019GXNSFFA245014, AD18216004, and AD18281079"]}]},{"name":"Guangxi Key Laboratory of Image and Graphic Intelligent Processing","award":["GIIP2003"],"award-info":[{"award-number":["GIIP2003"]}]},{"name":"GUET Excellent Graduate Thesis Program","award":["18YJPYSS15"],"award-info":[{"award-number":["18YJPYSS15"]}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["ACM Trans. Multimedia Comput. Commun. Appl."],"published-print":{"date-parts":[[2021,1,31]]},"abstract":"<jats:p>\n            Chinese image description generation tasks usually have some challenges, such as single-feature extraction, lack of global information, and lack of detailed description of the image content. To address these limitations, we propose a fuzzy attention-based DenseNet-BiLSTM Chinese image captioning method in this article. In the proposed method, we first improve the densely connected network to extract features of the image at different scales and to enhance the model\u2019s ability to capture the weak features. At the same time, a bidirectional LSTM is used as the decoder to enhance the use of context information. The introduction of an improved fuzzy attention mechanism effectively improves the problem of correspondence between image features and contextual information. We conduct experiments on the AI Challenger dataset to evaluate the performance of the model. The results show that compared with other models, our proposed model achieves higher scores in objective quantitative evaluation indicators, including BLEU\n            <jats:inline-formula>\n              <jats:alternatives>\n                <jats:tex-math>\n                  \n                <\/jats:tex-math>\n              <\/jats:alternatives>\n            <\/jats:inline-formula>\n            , BLEU\n            <jats:inline-formula>\n              <jats:alternatives>\n                <jats:tex-math>\n                  \n                <\/jats:tex-math>\n              <\/jats:alternatives>\n            <\/jats:inline-formula>\n            , METEOR, ROUGEl, and CIDEr. The generated description sentence can accurately express the image content.\n          <\/jats:p>","DOI":"10.1145\/3422668","type":"journal-article","created":{"date-parts":[[2021,4,1]],"date-time":"2021-04-01T01:53:55Z","timestamp":1617242035000},"page":"1-18","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":90,"title":["Chinese Image Captioning via Fuzzy Attention-based DenseNet-BiLSTM"],"prefix":"10.1145","volume":"17","author":[{"given":"Huimin","family":"Lu","sequence":"first","affiliation":[{"name":"Guilin University of Electronic Technology and Kyushu Institute of Technology, Kitakyushu, Japan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Rui","family":"Yang","sequence":"additional","affiliation":[{"name":"Guilin University of Electronic Technology, Guilin, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhenrong","family":"Deng","sequence":"additional","affiliation":[{"name":"Guilin University of Electronic Technology, Guilin, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yonglin","family":"Zhang","sequence":"additional","affiliation":[{"name":"Guilin University of Electronic Technology, Guilin, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Guangwei","family":"Gao","sequence":"additional","affiliation":[{"name":"Nanjing University of Posts and Telecommunications, Nanjing, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9488-8236","authenticated-orcid":false,"given":"Rushi","family":"Lan","sequence":"additional","affiliation":[{"name":"Guilin University of Electronic Technology and South China University of Technology, Guangzhou, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2021,3,31]]},"reference":[{"key":"e_1_2_1_1_1","doi-asserted-by":"publisher","DOI":"10.5555\/1858681.1858808"},{"key":"e_1_2_1_2_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.ins.2012.07.022"},{"key":"e_1_2_1_3_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00636"},{"key":"e_1_2_1_4_1","unstructured":"Dzmitry Bahdanau Kyunghyun Cho and Yoshua Bengio. 2014. 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