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However, current methods often use the co-occurrence probability of labels based on the training set as the adjacency matrix to model this correlation, which is greatly limited by the dataset and affects the model\u2019s generalization ability. This article proposes a Graph Attention Transformer Network, a general framework for multi-label image classification by mining rich and effective label correlation. First, we use the cosine similarity value of the pre-trained label word embedding as the initial correlation matrix, which can represent richer semantic information than the co-occurrence one. Subsequently, we propose the graph attention transformer layer to transfer this adjacency matrix to adapt to the current domain. Our extensive experiments have demonstrated that our proposed methods can achieve highly competitive performance on three datasets.<\/jats:p>","DOI":"10.1145\/3578518","type":"journal-article","created":{"date-parts":[[2022,12,29]],"date-time":"2022-12-29T12:09:48Z","timestamp":1672315788000},"page":"1-16","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":47,"title":["Graph Attention Transformer Network for Multi-label Image Classification"],"prefix":"10.1145","volume":"19","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-9954-0693","authenticated-orcid":false,"given":"Jin","family":"Yuan","sequence":"first","affiliation":[{"name":"Southeast University, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7821-7056","authenticated-orcid":false,"given":"Shikai","family":"Chen","sequence":"additional","affiliation":[{"name":"Southeast University, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8759-4811","authenticated-orcid":false,"given":"Yao","family":"Zhang","sequence":"additional","affiliation":[{"name":"University of Chinese Academy of Sciences, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5216-3827","authenticated-orcid":false,"given":"Zhongchao","family":"Shi","sequence":"additional","affiliation":[{"name":"Lenovo Research, Beijing, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7729-0622","authenticated-orcid":false,"given":"Xin","family":"Geng","sequence":"additional","affiliation":[{"name":"Southeast University, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2290-1785","authenticated-orcid":false,"given":"Jianping","family":"Fan","sequence":"additional","affiliation":[{"name":"Lenovo Research, Beijing, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9142-5914","authenticated-orcid":false,"given":"Yong","family":"Rui","sequence":"additional","affiliation":[{"name":"Lenovo Research, Beijing, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2023,2,27]]},"reference":[{"key":"e_1_3_1_2_2","volume-title":"Proceedings of the International Conference on Learning Representations (ICLR\u201915)","author":"Bahdanau Dzmitry","year":"2015","unstructured":"Dzmitry Bahdanau, Kyunghyun Cho, and Yoshua Bengio. 2015. 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