The DOI matches its Crossref record; 68 of the 68 references it cites were found in a registry or an index.
A Discriminatively Learned CNN Embedding for Person Reidentification
Open accessAbstract
In this article, we revisit two popular convolutional neural networks in person re-identification (re-ID): verification and identification models. The two models have their respective advantages and limitations due to different loss functions. Here, we shed light on how to combine the two models to learn more discriminative pedestrian descriptors. Specifically, we propose a Siamese network that simultaneously computes the identification loss and verification loss. Given a pair of training images, the network predicts the identities of the two input images and whether they belong to the same identity. Our network learns a discriminative embedding and a similarity measurement at the same time, thus taking full usage of the re-ID annotations. Our method can be easily applied on different pretrained networks. Albeit simple, the learned embedding improves the state-of-the-art performance on two public person re-ID benchmarks. Further, we show that our architecture can also be applied to image retrieval. The code is available at https://github.com/layumi/2016_person_re-ID.
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Cites
Deep Residual Learning for Image Recognition
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ImageNet Large Scale Visual Recognition Challenge
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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
Self-citationObject retrieval with large vocabularies and fast spatial matching
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Cited by
Person Transfer GAN to Bridge Domain Gap for Person Re-identification
Wei, L., Zhang, S., Gao, W., & Tian, Q. (2018) · 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2018.00016 · Cited by 2,118
FairMOT: On the Fairness of Detection and Re-identification in Multiple Object Tracking
Zhang, Y., Wang, C., Wang, X., Zeng, W., & Liu, W. (2021) · International Journal of Computer Vision · doi:10.1007/s11263-021-01513-4 · Cited by 1,861
Bag of Tricks and a Strong Baseline for Deep Person Re-Identification
Luo, H., Gu, Y., Liao, X., Lai, S., & Jiang, W. (2019) · 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw.2019.00190 · Cited by 1,584
TransReID: Transformer-based Object Re-Identification
He, S., Luo, H., Wang, P., Wang, F., Li, H., & Jiang, W. (2021) · 2021 IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv48922.2021.01474 · Cited by 1,244
MOTS: Multi-Object Tracking and Segmentation
Voigtlaender, P., Krause, M., Osep, A., Luiten, J., Sekar, B. B. G., Geiger, A., & Leibe, B. (2019) · 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2019.00813 · Cited by 658
Metadata
- Authors
- Zheng, Z., Zheng, L., & Yang, Y.
- Venue
- ACM Transactions on Multimedia Computing, Communications, and Applications
- Published
- 2017-12-13
- Volume
- 14
- Issue
- 1
- Pages
- 1-20
- ISSN
- 1551-6857 (ISSN-L)
- 1551-6865
- OpenAlex
- W3098711604
- Cited by
- 874
- References
- 68
- FWCI
- 46.84
- Type
- journal article
- Language
- en
- Open access
- green
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