A synthetic dataset for person re-identification and attribute recognition.
Onur Can Uner, Cem Aslan, Burak Ercan, Tayfun Ates, Ufuk Celikcan, Aykut Erdem, and Erkut Erdem. "Synthetic18K: Learning Better Representations for Person Re-ID and Attribute Recognition from 1.4 Million Synthetic Images", Signal Processing: Image Communication, in press.
Paper | Bibtex
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.
This work was supported in part by GEBIP 2018 Award of the Turkish Academy of Sciences to E. Erdem, BAGEP 2021 Award of the Science Academy to A. Erdem, and by TUBITAK-1001 Program Award No. 217E029.