The DOI matches its Crossref record; 31 of the 31 references it cites were found in a registry or an index.
Cross-Modality Person Re-Identification with Generative Adversarial Training
Open accessAbstract
Person re-identification (Re-ID) is an important task in video surveillance which automatically searches and identifies people across different cameras. Despite the extensive Re-ID progress in RGB cameras, few works have studied the Re-ID between infrared and RGB images, which is essentially a cross-modality problem and widely encountered in real-world scenarios. The key challenge lies in two folds, i.e., the lack of discriminative information to re-identify the same person between RGB and infrared modalities, and the difficulty to learn a robust metric towards such a large-scale cross-modality retrieval. In this paper, we tackle the above two challenges by proposing a novel cross-modality generative adversarial network (termed cmGAN). To handle the issue of insufficient discriminative information, we leverage the cutting-edge generative adversarial training to design our own discriminator to learn discriminative feature representation from different modalities. To handle the issue of large-scale cross-modality metric learning, we integrates both identification loss and cross-modality triplet loss, which minimize inter-class ambiguity while maximizing cross-modality similarity among instances. The entire cmGAN can be trained in an end-to-end manner by using standard deep neural network framework. We have quantized the performance of our work in the newly-released SYSU RGB-IR Re-ID benchmark, and have reported superior performance, i.e., Cumulative Match Characteristic curve (CMC) and Mean Average Precision (MAP), over the state-of-the-art works [Wu et al., 2017], respectively.
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Cites
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Image-to-Image Translation with Conditional Adversarial Networks
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Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network
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Person re-identification by Local Maximal Occurrence representation and metric learning
Liao, S., Hu, Y., Zhu, X., & Li, S. Z. (2015) · 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2015.7298832 · Cited by 2,091
Large scale metric learning from equivalence constraints
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Person re-identification by symmetry-driven accumulation of local features
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Cited by
RGB-Infrared Cross-Modality Person Re-Identification via Joint Pixel and Feature Alignment
Wang, G., Zhang, T., Cheng, J., Liu, S., Yang, Y., & Hou, Z. (2019) · 2019 IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2019.00372 · Cited by 559
Dynamic Dual-Attentive Aggregation Learning for Visible-Infrared Person Re-identification
Ye, M., Shen, J., J. Crandall, D., Shao, L., & Luo, J. (2020) · Lecture Notes in Computer Science · doi:10.1007/978-3-030-58520-4_14 · Cited by 523
Zhao, F., Zhang, C., & Geng, B. (2024) · ACM Computing Surveys · doi:10.1145/3649447 · Cited by 480
Channel Augmented Joint Learning for Visible-Infrared Recognition
Ye, M., Ruan, W., Du, B., & Shou, M. Z. (2021) · 2021 IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv48922.2021.01331 · Cited by 375
Bi-Directional Center-Constrained Top-Ranking for Visible Thermal Person Re-Identification
Ye, M., Lan, X., Wang, Z., & Yuen, P. C. (2020) · IEEE Transactions on Information Forensics and Security · doi:10.1109/tifs.2019.2921454 · Cited by 331
Metadata
- Authors
- Dai, P., Ji, R., Wang, H., Wu, Q., & Huang, Y.
- Venue
- Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence
- Published
- 2018-07
- Pages
- 677-683
- OpenAlex
- W2808260522
- Cited by
- 425
- References
- 31
- FWCI
- 11.38
- Type
- conference paper
- Language
- en
- Open access
- bronze
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- Dai, P., Ji, R., Wang, H., Wu, Q., & Huang, Y. (2018). Cross-Modality Person Re-Identification with Generative Adversarial Training. Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence. https://doi.org/10.24963/ijcai.2018/94
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