In:
JUSTC, Journal of University of Science and Technology of China, Vol. 52, No. 12 ( 2022), p. 7-
Abstract:
In recent years, unsupervised person reidentification technology has made great strides. The technology retrieves images of interested persons under different cameras from massive repositories of unlabeled images. However, in the current research, there are some existing problems, such as the influence of pedestrians appearing across cameras and pseudo-label noise. To solve these problems, we conduct research in two ways: removing the camera bias and dynamically updating the memory model. In removing the camera bias, based on a learnable channel attention module, the features that are only related to cameras can be extracted from the feature map, thereby removing the camera bias in the global features and obtaining the features that can represent the pedestrians. In regards to dynamically updating the memory model, since the instance features do not necessarily belong to the identity represented by the pseudo-label, we adopt a method to update the memory dynamically according to the distance between the instance features and the category features so that the category features tend to be true. We combine the removal of the camera bias and the dynamic updating of the memory model to better solve problems in this field. Extensive experimentation demonstrates the superiority of our method over the state-of-the-art approaches on fully unsupervised Re-ID tasks.
Type of Medium:
Online Resource
ISSN:
0253-2778
,
0253-2778
DOI:
10.52396/JUSTC-2022-0015
Language:
Unknown
Publisher:
Journal of University of Science and Technology of China
Publication Date:
2022
detail.hit.zdb_id:
2719801-7
SSG:
11
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