The DOI matches its Crossref record; 40 of the 40 references it cites were found in a registry or an index.
Embedding Deep Metric for Person Re-identification: A Study Against Large Variations
Abstract
Person re-identification is challenging due to the large variations of pose, illumination, occlusion and camera view. Owing to these variations, the pedestrian data is distributed as highly-curved manifolds in the feature space, despite the current convolutional neural networks (CNN)’s capability of feature extraction. However, the distribution is unknown, so it is difficult to use the geodesic distance when comparing two samples. In practice, the current deep embedding methods use the Euclidean distance for the training and test. On the other hand, the manifold learning methods suggest to use the Euclidean distance in the local range, combining with the graphical relationship between samples, for approximating the geodesic distance. From this point of view, selecting suitable positive (i.e. intra-class) training samples within a local range is critical for training the CNN embedding, especially when the data has large intra-class variations. In this paper, we propose a novel moderate positive sample mining method to train robust CNN for person re-identification, dealing with the problem of large variation. In addition, we improve the learning by a metric weight constraint, so that the learned metric has a better generalization ability. Experiments show that these two strategies are effective in learning robust deep metrics for person re-identification, and accordingly our deep model significantly outperforms the state-of-the-art methods on several benchmarks of person re-identification. Therefore, the study presented in this paper may be useful in inspiring new designs of deep models for person re-identification.
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Cited by
Pose-Driven Deep Convolutional Model for Person Re-identification
Su, C., Li, J., Zhang, S., Xing, J., Gao, W., & Tian, Q. (2017) · 2017 IEEE International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2017.427 · Cited by 832
Attention-Aware Compositional Network for Person Re-identification
Xu, J., Zhao, R., Zhu, F., Wang, H., & Ouyang, W. (2018) · 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2018.00226 · Cited by 502
Deep Metric Learning with Hierarchical Triplet Loss
Ge, W., Huang, W., Dong, D., & Scott, M. R. (2018) · Lecture Notes in Computer Science · doi:10.1007/978-3-030-01231-1_17 · Cited by 445
Learning With Average Precision: Training Image Retrieval With a Listwise Loss
Revaud, J., Almazan, J., Rezende, R., & Souza, C. D. (2019) · 2019 IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2019.00521 · Cited by 408
Scalable Person Re-identification on Supervised Smoothed Manifold
Bai, S., Bai, X., & Tian, Q. (2017) · 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2017.358 · Cited by 320
Metadata
- Authors
- Shi, H., Yang, Y., Zhu, X., Liao, S., Lei, Z., Zheng, W., & Li, S. Z.
- Venue
- Lecture Notes in Computer Science
- Publisher
- Springer International Publishing
- Published
- 2016
- Pages
- 732-748
- ISSN
- 0302-9743 (ISSN-L)
- 1611-3349
- OpenAlex
- W2519373641
- Cited by
- 257
- References
- 40
- FWCI
- 16.09
- Type
- conference paper
- Language
- en
- Open access
- closed
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- Shi, H., Yang, Y., Zhu, X., Liao, S., Lei, Z., Zheng, W., & Li, S. Z. (2016). Embedding Deep Metric for Person Re-identification: A Study Against Large Variations. Lecture Notes in Computer Science. https://doi.org/10.1007/978-3-319-46448-0_44
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