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Neural Computing and Applications

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23.11.2020 | S.I.: Deep Social Computing

Transferring fashion to surveillance with weak labels

verfasst von: Qi Zheng, Zheng He, Chao Liang, Jun Chen, Chia-Wen Lin, Dapeng Tao

Erschienen in: Neural Computing and Applications | Ausgabe 18/2023

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Abstract

In this paper, we address the problem of automatic clothing parsing in surveillance images using the information from user-generated tags, such as “jeans” and “T-shirt.” Although clothing parsing has achieved great success in the fashion domain, it is quite challenging to parse target under practical surveillance conditions due to the presence of complex environmental interference, such as that from low resolution, viewpoint variations and lighting changes. Our method is developed to capture target information from the fashion domain and apply this information to a surveillance domain by weakly supervised transfer learning. Most target tags convey strong location information (e.g., “T-shirt” is always shown in the upper region), which can be used as weak labels for our transfer method. Both quantitative and qualitative experiments conducted on practical surveillance datasets demonstrate the effectiveness of the proposed surveillance data enhancing method.

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Titel: Transferring fashion to surveillance with weak labels
verfasst von: Qi Zheng
Zheng He
Chao Liang
Jun Chen
Chia-Wen Lin
Dapeng Tao
Publikationsdatum: 23.11.2020
Verlag: Springer London
Erschienen in: Neural Computing and Applications / Ausgabe 18/2023
Print ISSN: 0941-0643
Elektronische ISSN: 1433-3058
DOI: https://doi.org/10.1007/s00521-020-05528-9

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