Adaptive Exploration for Unsupervised Person Re-identification

Ding，Yuhang1; Fan，Hehe2; Xu，Mingliang3; Yang，Yi2

2020-03-01

10.1145/3369393

ACM Transactions on Multimedia Computing Communications and Applications   影响因子和分区

1551-6857

1551-6865

Due to domain bias, directly deploying a deep person re-identification (re-ID) model trained on one dataset often achieves considerably poor accuracy on another dataset. In this article, we propose an Adaptive Exploration (AE) method to address the domain-shift problem for re-ID in an unsupervised manner. Specifically, in the target domain, the re-ID model is inducted to (1) maximize distances between all person images and (2) minimize distances between similar person images. In the first case, by treating each person image as an individual class, a non-parametric classifier with a feature memory is exploited to encourage person images to move far away from each other. In the second case, according to a similarity threshold, our method adaptively selects neighborhoods for each person image in the feature space. By treating these similar person images as the same class, the non-parametric classifier forces them to stay closer. However, a problem of the adaptive selection is that, when an image has too many neighborhoods, it is more likely to attract other images as its neighborhoods. As a result, a minority of images may select a large number of neighborhoods while a majority of images has only a few neighborhoods. To address this issue, we additionally integrate a balance strategy into the adaptive selection. We evaluate our methods with two protocols. The first one is called "target-only re-ID", in which only the unlabeled target data is used for training. The second one is called "domain adaptive re-ID", in which both the source data and the target data are used during training. Experimental results on large-scale re-ID datasets demonstrate the effectiveness of our method. Our code has been released at https://github.com/dyh127/Adaptive-Exploration-for-Unsupervised-Person-Re-Identification.

deep learning domain adaptation Person re-identification unsupervised learning

SCI ; EI

英语

第一

Computer Science

Computer Science, Information Systems ; Computer Science, Software Engineering ; Computer Science, Theory & Methods

WOS:000583712100002

ASSOC COMPUTING MACHINERY

20201608420861

Large dataset

Ergonomics and Human Factors Engineering:461.4 ; Data Processing and Image Processing:723.2

2-s2.0-85083085688

Scopus

1.SUSTech-UTS Joint Centre of CIS,Southern University of Science and Technology,China
2.Centre for Artificial Intelligence,University of Technology Sydney,Ultimo,Sydney,Australia
3.School of Information Engineering,Zhengzhou University,Zhengzhou, Henan,China

Ding，Yuhang,Fan，Hehe,Xu，Mingliang,et al. Adaptive Exploration for Unsupervised Person Re-identification[J]. ACM Transactions on Multimedia Computing Communications and Applications,2020,16(1).

Ding，Yuhang,Fan，Hehe,Xu，Mingliang,&Yang，Yi.(2020).Adaptive Exploration for Unsupervised Person Re-identification.ACM Transactions on Multimedia Computing Communications and Applications,16(1).

Ding，Yuhang,et al."Adaptive Exploration for Unsupervised Person Re-identification".ACM Transactions on Multimedia Computing Communications and Applications 16.1(2020).