The DOI matches its Crossref record; 45 of the 45 references it cites were found in a registry or an index.
Self-Training With Progressive Augmentation for Unsupervised Cross-Domain Person Re-Identification
Open accessAbstract
Person re-identification (Re-ID) has achieved great improvement with deep learning and a large amount of labelled training data. However, it remains a challenging task for adapting a model trained in a source domain of labelled data to a target domain of only unlabelled data available. In this work, we develop a self-training method with progressive augmentation framework (PAST) to promote the model performance progressively on the target dataset. Specially, our PAST framework consists of two stages, namely, conservative stage and promoting stage. The conservative stage captures the local structure of target-domain data points with triplet-based loss functions, leading to improved feature representations. The promoting stage continuously optimizes the network by appending a changeable classification layer to the last layer of the model, enabling the use of global information about the data distribution. Importantly, we propose a new self-training strategy that progressively augments the model capability by adopting conservative and promoting stages alternately. Furthermore, to improve the reliability of selected triplet samples, we introduce a ranking-based triplet loss in the conservative stage, which is a label-free objective function based on the similarities between data pairs. Experiments demonstrate that the proposed method achieves state-of-the-art person Re-ID performance under the unsupervised cross-domain setting.
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Cites
Deep Residual Learning for Image Recognition
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Self-citationImageNet: A large-scale hierarchical image database
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Object Detection with Discriminatively Trained Part-Based Models
Felzenszwalb, P. F., Girshick, R. B., McAllester, D., & Ramanan, D. (2010) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2009.167 · Cited by 9,853
Scalable Person Re-identification: A Benchmark
Zheng, L., Shen, L., Tian, L., Wang, S., Wang, J., & Tian, Q. (2015) · 2015 IEEE International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2015.133 · Cited by 4,506
Understanding deep learning requires rethinking generalization
Zhang, C., Bengio, S., Hardt, M., Recht, B., & Vinyals, O. (2016) · arXiv · doi:10.48550/arxiv.1611.03530 · Cited by 2,838 · cited 1×
“Since the percentage of corrupted labels largely affect the model generalization on the target dataset [42], we propose a method to improve the quality of labels in a progressive way which results in considerable improvement of model generalization on the unseen target dataset.”
MethodIn Defense of the Triplet Loss for Person Re-Identification
Hermans, A., Beyer, L., & Leibe, B. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1703.07737 · Cited by 2,687
Density-Based Clustering Based on Hierarchical Density Estimates
Campello, R. J. G. B., Moulavi, D., & Sander, J. (2013) · Lecture Notes in Computer Science · doi:10.1007/978-3-642-37456-2_14 · Cited by 2,680
DeepReID: Deep Filter Pairing Neural Network for Person Re-identification
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Beyond Part Models: Person Retrieval with Refined Part Pooling (and A Strong Convolutional Baseline)
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Cited by
Style Normalization and Restitution for Generalizable Person Re-Identification
Jin, X., Lan, C., Zeng, W., Chen, Z., & Zhang, L. (2020) · 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr42600.2020.00321 · Cited by 426
Unsupervised Person Re-Identification via Multi-Label Classification
Wang, D., & Zhang, S. (2020) · 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr42600.2020.01099 · Cited by 373
ICE: Inter-instance Contrastive Encoding for Unsupervised Person Re-identification
Chen, H., Lagadec, B., & Bremond, F. (2021) · 2021 IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv48922.2021.01469 · Cited by 283
Unsupervised person re-identification with multi-label learning guided self-paced clustering
Li, Q., Peng, X., Qiao, Y., & Hao, Q. (2022) · Pattern Recognition · doi:10.1016/j.patcog.2022.108521 · Cited by 35
Metadata
- Authors
- Zhang, X., Cao, J., Shen, C., & You, M.
- Venue
- 2019 IEEE/CVF International Conference on Computer Vision (ICCV)
- Publisher
- IEEE
- Published
- 2019-10
- Pages
- 8221-8230
- OpenAlex
- W3009761962
- Cited by
- 267
- References
- 45
- FWCI
- 16.53
- Type
- conference paper
- Language
- en
- Open access
- green
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- Zhang, X., Cao, J., Shen, C., & You, M. (2019). Self-Training With Progressive Augmentation for Unsupervised Cross-Domain Person Re-Identification. 2019 IEEE/CVF International Conference on Computer Vision (ICCV). https://doi.org/10.1109/iccv.2019.00831
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