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Firstly, a new two-stream parameter sharing network is proposed, by sharing the convolutional layers parameters to obtain more modality-sharing features. Secondly, a multi-granularity feature learning module is designed to reduce modality differences at both coarse and fine-grained levels while further enhancing feature discriminability. In addition, a center alignment loss is proposed to learn relationships between identities and to reduce modality differences by clustering features into their centers. For the part-level feature learning, the hetero-center triplet loss is adopted to alleviate the strict constraints of triplet loss. Finally, extensive experiments are conducted to validate our method outperforms state-of-the-art methods on two challenging datasets. In the SYSU-MM01 dataset, the Rank-1 and mAP reach <jats:inline-formula><jats:alternatives><jats:tex-math>$$74.0\\%$$<\/jats:tex-math><mml:math xmlns:mml=\"http:\/\/www.w3.org\/1998\/Math\/MathML\">\n                  <mml:mrow>\n                    <mml:mn>74.0<\/mml:mn>\n                    <mml:mo>%<\/mml:mo>\n                  <\/mml:mrow>\n                <\/mml:math><\/jats:alternatives><\/jats:inline-formula> and <jats:inline-formula><jats:alternatives><jats:tex-math>$$70.51\\%$$<\/jats:tex-math><mml:math xmlns:mml=\"http:\/\/www.w3.org\/1998\/Math\/MathML\">\n                  <mml:mrow>\n                    <mml:mn>70.51<\/mml:mn>\n                    <mml:mo>%<\/mml:mo>\n                  <\/mml:mrow>\n                <\/mml:math><\/jats:alternatives><\/jats:inline-formula> in the all-search mode, which is an increase of <jats:inline-formula><jats:alternatives><jats:tex-math>$$3.4\\%$$<\/jats:tex-math><mml:math xmlns:mml=\"http:\/\/www.w3.org\/1998\/Math\/MathML\">\n                  <mml:mrow>\n                    <mml:mn>3.4<\/mml:mn>\n                    <mml:mo>%<\/mml:mo>\n                  <\/mml:mrow>\n                <\/mml:math><\/jats:alternatives><\/jats:inline-formula> and <jats:inline-formula><jats:alternatives><jats:tex-math>$$3.61\\%$$<\/jats:tex-math><mml:math xmlns:mml=\"http:\/\/www.w3.org\/1998\/Math\/MathML\">\n                  <mml:mrow>\n                    <mml:mn>3.61<\/mml:mn>\n                    <mml:mo>%<\/mml:mo>\n                  <\/mml:mrow>\n                <\/mml:math><\/jats:alternatives><\/jats:inline-formula> to baseline, respectively.<\/jats:p>","DOI":"10.1007\/s40747-023-01189-y","type":"journal-article","created":{"date-parts":[[2023,8,14]],"date-time":"2023-08-14T20:04:16Z","timestamp":1692043456000},"page":"949-962","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":7,"title":["Parameter sharing and multi-granularity feature learning for cross-modality person re-identification"],"prefix":"10.1007","volume":"10","author":[{"given":"Sixian","family":"Chan","sequence":"first","affiliation":[]},{"given":"Feng","family":"Du","sequence":"additional","affiliation":[]},{"given":"Tinglong","family":"Tang","sequence":"additional","affiliation":[]},{"given":"Guodao","family":"Zhang","sequence":"additional","affiliation":[]},{"given":"Xiaoliang","family":"Jiang","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6928-3040","authenticated-orcid":false,"given":"Qiu","family":"Guan","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2023,8,14]]},"reference":[{"key":"1189_CR1","doi-asserted-by":"publisher","unstructured":"Sreenu G, Durai MAS (2019) Intelligent video surveillance: a review through deep learning techniques for crowd analysis. 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