Meta Clustering Learning for Large-scale Unsupervised Person
Re-identification
- URL: http://arxiv.org/abs/2111.10032v1
- Date: Fri, 19 Nov 2021 04:10:18 GMT
- Title: Meta Clustering Learning for Large-scale Unsupervised Person
Re-identification
- Authors: Xin Jin, Tianyu He, Zhiheng Yin, Xu Shen, Tongliang Liu, Xinchao Wang,
Jianqiang Huang, Xian-Sheng Hua, Zhibo Chen
- Abstract summary: We propose a "small data for big task" paradigm dubbed Meta Clustering Learning (MCL)
MCL only pseudo-labels a subset of the entire unlabeled data via clustering to save computing for the first-phase training.
Our method significantly saves computational cost while achieving a comparable or even better performance compared to prior works.
- Score: 124.54749810371986
- License: http://creativecommons.org/publicdomain/zero/1.0/
- Abstract: Unsupervised Person Re-identification (U-ReID) with pseudo labeling recently
reaches a competitive performance compared to fully-supervised ReID methods
based on modern clustering algorithms. However, such clustering-based scheme
becomes computationally prohibitive for large-scale datasets. How to
efficiently leverage endless unlabeled data with limited computing resources
for better U-ReID is under-explored. In this paper, we make the first attempt
to the large-scale U-ReID and propose a "small data for big task" paradigm
dubbed Meta Clustering Learning (MCL). MCL only pseudo-labels a subset of the
entire unlabeled data via clustering to save computing for the first-phase
training. After that, the learned cluster centroids, termed as meta-prototypes
in our MCL, are regarded as a proxy annotator to softly annotate the rest
unlabeled data for further polishing the model. To alleviate the potential
noisy labeling issue in the polishment phase, we enforce two well-designed loss
constraints to promise intra-identity consistency and inter-identity strong
correlation. For multiple widely-used U-ReID benchmarks, our method
significantly saves computational cost while achieving a comparable or even
better performance compared to prior works.
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