{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,27]],"date-time":"2026-01-27T12:36:03Z","timestamp":1769517363527,"version":"3.49.0"},"reference-count":62,"publisher":"Institution of Engineering and Technology (IET)","issue":"8","license":[{"start":{"date-parts":[[2023,5,4]],"date-time":"2023-05-04T00:00:00Z","timestamp":1683158400000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100003392","name":"Natural Science Foundation of Fujian Province","doi-asserted-by":"publisher","award":["3502Z20206068"],"award-info":[{"award-number":["3502Z20206068"]}],"id":[{"id":"10.13039\/501100003392","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100020227","name":"Youth Innovation Foundation of Xiamen","doi-asserted-by":"publisher","award":["2020H0023"],"award-info":[{"award-number":["2020H0023"]}],"id":[{"id":"10.13039\/100020227","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["ietresearch.onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["IET Computer Vision"],"published-print":{"date-parts":[[2023,12]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>Siamese networks have attracted wide attention in visual tracking due to their competitive accuracy and speed. However, the existing Siamese trackers usually leverage a fixed linear aggregation of feature maps, which does not effectively fuse the different layers of features with attention. Besides, most of Siamese trackers calculate the similarity between the template and the search region through a cross\u2010correlation operation between the features of the last blocks from the two branches, which might introduce the redundant noise information. In order to solve these problems, this study proposes a novel Siamese visual tracking method via cross\u2010layer calibration fusion, termed SiamCCF. An attention\u2010based feature fusion module is employed using local attention and non\u2010local attention to fuse the features from the deep and shallow layers, so as to capture both local details and high\u2010level semantic information. Moreover, a cross\u2010layer calibration module can use the fused features to calibrate the features of the last network blocks and build the cross\u2010layer long\u2010range spatial and inter\u2010channel dependencies around each spatial location. Extensive experiments demonstrate that the proposed method has achieved competitive tracking performance compared with state\u2010of\u2010the\u2010art trackers on challenging benchmarks, including OTB100, OTB2013, UAV123, UAV20L, and LaSOT.<\/jats:p>","DOI":"10.1049\/cvi2.12201","type":"journal-article","created":{"date-parts":[[2023,5,4]],"date-time":"2023-05-04T03:30:01Z","timestamp":1683171001000},"page":"869-882","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["SiamCCF: Siamese visual tracking via cross\u2010layer calibration fusion"],"prefix":"10.1049","volume":"17","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-5631-7942","authenticated-orcid":false,"given":"Si","family":"Chen","sequence":"first","affiliation":[{"name":"Fujian Key Laboratory of Pattern Recognition and Image Understanding School of Computer and Information Engineering Xiamen University of Technology  Xiamen 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