The DOI matches its Crossref record; 52 of the 52 references it cites were found in a registry or an index.
Scalable Person Re-identification on Supervised Smoothed Manifold
Open accessAbstract
Most existing person re-identification algorithms either extract robust visual features or learn discriminative metrics for person images. However, the underlying manifold which those images reside on is rarely investigated. That arises a problem that the learned metric is not smooth with respect to the local geometry structure of the data manifold. In this paper, we study person re-identification with manifold-based affinity learning, which did not receive enough attention from this area. An unconventional manifold-preserving algorithm is proposed, which can 1) make best use of supervision from training data, whose label information is given as pairwise constraints, 2) scale up to large repositories with low on-line time complexity, and 3) be plunged into most existing algorithms, serving as a generic postprocessing procedure to further boost the identification accuracies. Extensive experimental results on five popular person re-identification benchmarks consistently demonstrate the effectiveness of our method. Especially, on the largest CUHK03 and Market-1501, our method outperforms the state-of-the-art alternatives by a large margin with high efficiency, which is more appropriate for practical applications.
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Cites
Deep Residual Learning for Image Recognition
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Scalable Person Re-identification: A Benchmark
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Self-citationLearning with Local and Global Consistency
Zhou, D., Bousquet, O., Lal, T. N., Weston, J., & Schölkopf, B. (2003) · MPG.PuRe (Max Planck Society) · Cited by 3,864
DeepReID: Deep Filter Pairing Neural Network for Person Re-identification
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Person re-identification by Local Maximal Occurrence representation and metric learning
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Self-Tuning Spectral Clustering
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Cited by
Person Re-identification in the Wild
Zheng, L., Zhang, H., Sun, S., Chandraker, M., Yang, Y., & Tian, Q. (2017) · 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2017.357 · Cited by 830
Self-citationHuman Semantic Parsing for Person Re-identification
Kalayeh, M. M., Basaran, E., Gokmen, M., Kamasak, M. E., & Shah, M. (2018) · 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2018.00117 · Cited by 584
ABD-Net: Attentive but Diverse Person Re-Identification
Chen, T., Ding, S., Xie, J., Yuan, Y., Chen, W., Yang, Y., Ren, Z., & Wang, Z. (2019) · 2019 IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2019.00844 · Cited by 561
Dual Attention Matching Network for Context-Aware Feature Sequence Based Person Re-identification
Si, J., Zhang, H., Li, C.-G., Kuen, J., Kong, X., Kot, A. C., & Wang, G. (2018) · 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2018.00562 · Cited by 466
AlignedReID: Surpassing Human-Level Performance in Person Re-Identification
Zhang, X., Luo, H., Fan, X., Xiang, W., Sun, Y., Xiao, Q., Jiang, W., Zhang, C., & Sun, J. (2017) · arXiv · doi:10.48550/arxiv.1711.08184 · Cited by 419
Metadata
- Authors
- Bai, S., Bai, X., & Tian, Q.
- Venue
- 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
- Publisher
- IEEE
- Published
- 2017-07
- Pages
- 3356-3365
- ISSN
- 1063-6919 (ISSN-L)
- OpenAlex
- W2604211872
- Cited by
- 320
- References
- 52
- FWCI
- 17.55
- Type
- conference paper
- Language
- en
- Open access
- green
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- Bai, S., Bai, X., & Tian, Q. (2017). Scalable Person Re-identification on Supervised Smoothed Manifold. 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). https://doi.org/10.1109/cvpr.2017.358
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