Two-phase self-supervised pretraining for object re-identification

Ma, Haoyan; Li, Xiang; Yuan, Xia; Zhao, Chenglin

doi:10.1016/j.knosys.2022.110220

Cited by 5 publications

(1 citation statement)

References 24 publications

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“…Vehicle re-identification (Re-ID) refers to judging whether vehicle images captured in non-overlapping areas belong to the same vehicle in a traffic monitoring scene within a specific range. Recently, vehicle re-identification methods based on supervised learning have made great progress [1][2][3][4][5]. However, the supervised learning method mainly has the following problems: (1) It is extremely dependent on complete labels, that is, the labels of training data from multiple non-overlapping cameras, annotating all large-scale unlabeled data, which is time-consuming and labor-intensive.…”

Section: Introductionmentioning

confidence: 99%

Unsupervised Vehicle Re-Identification Method Based on Source-Free Knowledge Transfer

Song,

Li,

Chen

et al. 2023

Applied Sciences

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The unsupervised domain-adaptive vehicle re-identification approach aims to transfer knowledge from a labeled source domain to an unlabeled target domain; however, there are knowledge differences between the target domain and the source domain. To mitigate domain discrepancies, existing unsupervised domain-adaptive re-identification methods typically require access to source domain data to assist in retraining the target domain model. However, for security reasons, such as data privacy, data exchange between different domains is often infeasible in many scenarios. To this end, this paper proposes an unsupervised domain-adaptive vehicle re-identification method based on source-free knowledge transfer. First, by constructing a source-free domain knowledge migration module, the target domain is consistent with the source domain model output to train a generator to generate the “source-like samples”. Then, it can effectively reduce the model knowledge difference and improve the model’s generalization performance. In the experiment, two mainstream public datasets in this field, VeRi776 and VehicleID, are tested experimentally, and the obtained rank-k (the cumulative matching features) and mAP (the mean Average Precision) indicators are both improved, which are suitable for object re-identification tasks when data between domains cannot be interoperated.

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