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

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 obv...

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Bibliographic Details
Published in:Signal processing. Image communication 2021-09, Vol.97, p.116335, Article 116335
Main Authors: Uner, Onur Can, Aslan, Cem, Ercan, Burak, Ates, Tayfun, Celikcan, Ufuk, Erdem, Aykut, Erdem, Erkut
Format: Article
Language:English
Subjects:
Annotations
Attribute recognition
Datasets
Learning
Object recognition
Person re-identification
Representations
Synthetic data
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Summary: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. •Our work learns robust representations for person re-id and attribute recognition.•We introduce Synthetic18K dataset for person re-id and attribute recognition.•Synthetic18K provides a strong alternative to the widely used ImageNet pre-training.•Our pre-trained models give highly competitive results against the state-of-the-art.
ISSN:0923-5965
1879-2677
DOI:10.1016/j.image.2021.116335