The DOI matches its Crossref record; 39 of the 39 references it cites were found in a registry or an index.
Joint Learning of Single-Image and Cross-Image Representations for Person Re-identification
Open accessAbstract
Person re-identification has been usually solved as either the matching of single-image representation (SIR) or the classification of cross-image representation (CIR). In this work, we exploit the connection between these two categories of methods, and propose a joint learning frame-work to unify SIR and CIR using convolutional neural network (CNN). Specifically, our deep architecture contains one shared sub-network together with two sub-networks that extract the SIRs of given images and the CIRs of given image pairs, respectively. The SIR sub-network is required to be computed once for each image (in both the probe and gallery sets), and the depth of the CIR sub-network is required to be minimal to reduce computational burden. Therefore, the two types of representation can be jointly optimized for pursuing better matching accuracy with moderate computational cost. Furthermore, the representations learned with pairwise comparison and triplet comparison objectives can be combined to improve matching performance. Experiments on the CUHK03, CUHK01 and VIPeR datasets show that the proposed method can achieve favorable accuracy while compared with state-of-the-arts.
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Cites
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Cited by
Harmonious Attention Network for Person Re-identification
Li, W., Zhu, X., & Gong, S. (2018) · 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2018.00243 · Cited by 1,449
A Discriminatively Learned CNN Embedding for Person Reidentification
Zheng, Z., Zheng, L., & Yang, Y. (2018) · ACM Transactions on Multimedia Computing, Communications, and Applications · doi:10.1145/3159171 · Cited by 874
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
GLAD: Global-Local-Alignment Descriptor for Pedestrian Retrieval
Wei, L., Zhang, S., Yao, H., Gao, W., & Tian, Q. (2017) · Proceedings of the 25th ACM international conference on Multimedia · doi:10.1145/3123266.3123279 · Cited by 409
CVM-Net: Cross-View Matching Network for Image-Based Ground-to-Aerial Geo-Localization
Hu, S., Feng, M., Nguyen, R. M. H., & Lee, G. H. (2018) · 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2018.00758 · Cited by 394
Metadata
- Authors
- Wang, F., Zuo, W., Lin, L., Zhang, D., & Zhang, L.
- Venue
- 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
- Publisher
- IEEE
- Published
- 2016-06
- Pages
- 1288-1296
- ISSN
- 1063-6919 (ISSN-L)
- OpenAlex
- W2471048925
- Cited by
- 389
- References
- 39
- FWCI
- 27.25
- Type
- conference paper
- Language
- en
- Open access
- green
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