Learning deep embedding with mini-cluster loss for person re-identification
Learning deep embedding with mini-cluster loss for person re-identification
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Publisher
New York: Springer US
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Language
English
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Publisher
New York: Springer US
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Recently, the triplet loss is commonly used in many deep person re-identification (ReID) frameworks to learn an embedding space in which similar data points are close and dissimilar data points are far away. However, the triplet loss simply focuses on the relative orders of points. This may lead to a relatively large intra-class variance and then a...
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Full title
Learning deep embedding with mini-cluster loss for person re-identification
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TN_cdi_proquest_journals_2191235567
Permalink
https://devfeature-collection.sl.nsw.gov.au/record/TN_cdi_proquest_journals_2191235567
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ISSN
1380-7501
E-ISSN
1573-7721
DOI
10.1007/s11042-019-7446-2
