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      Synthetic18K: Learning better representations for person re-ID and attribute recognition from 1.4 million synthetic images

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      Embargo Lift Date: 2023-05-26
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      Author(s)
      Aslan, C.
      Ercan, B.
      Ates, T.
      Celikcan, U.
      Erdem, A.
      Erdem, E.
      Üner, Onur Can
      Date
      2021-05-26
      Source Title
      Signal Processing: Image Communication
      Print ISSN
      0923-5965
      Publisher
      Elsevier
      Volume
      97
      Pages
      1 - 11
      Language
      English
      Type
      Article
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      Abstract
      Learning robust representations is critical for the success of person re-identification and attribute recognition systems. However, to achieve this, we must use a large dataset of diverse person images as well as annotations of identity labels and/or a set of different attributes. Apart from the obvious concerns about privacy issues, the manual annotation process is both time consuming and too costly. In this paper, we instead propose to use synthetic person images for addressing these difficulties. Specifically, we first introduce Synthetic18K, a large-scale dataset of over 1 million computer generated person images of 18K unique identities with relevant attributes. Moreover, we demonstrate that pretraining of simple deep architectures on Synthetic18K for person re-identification and attribute recognition and then fine-tuning on real data leads to significant improvements in prediction performances, giving results better than or comparable to state-of-the-art models.
      Keywords
      Person re-identification
      Attribute recognition
      Synthetic data
      Permalink
      http://hdl.handle.net/11693/77593
      Published Version (Please cite this version)
      https://doi.org/10.1016/j.image.2021.116335
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      • Department of Computer Engineering 1561
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