The DOI matches its Crossref record; 10 of the 10 references it cites were found in a registry or an index.
RGB-Infrared Cross-Modality Person Re-Identification via Joint Pixel and Feature Alignment
Open accessAbstract
RGB-Infrared (IR) person re-identification is an important and challenging task due to large cross-modality variations between RGB and IR images. Most conventional approaches aim to bridge the cross-modality gap with feature alignment by feature representation learning. Different from existing methods, in this paper, we propose a novel and end-to-end Alignment Generative Adversarial Network (AlignGAN) for the RGB-IR RE-ID task. The proposed model enjoys several merits. First, it can exploit pixel alignment and feature alignment jointly. To the best of our knowledge, this is the first work to model the two alignment strategies jointly for the RGB-IR RE-ID problem. Second, the proposed model consists of a pixel generator, a feature generator and a joint discriminator. By playing a min-max game among the three components, our model is able to not only alleviate the cross-modality and intra-modality variations, but also learn identity-consistent features. Extensive experimental results on two standard benchmarks demonstrate that the proposed model performs favourably against state-of-the-art methods. Especially, on SYSU-MM01 dataset, our model can achieve an absolute gain of 15.4% and 12.9% in terms of Rank-1 and mAP.
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Cites
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ImageNet Large Scale Visual Recognition Challenge
Russakovsky, O., Deng, J., Su, H., Krause, J., Satheesh, S., Ma, S., Huang, Z., Karpathy, A., Khosla, A., Bernstein, M., et al. (2015) · International Journal of Computer Vision · doi:10.1007/s11263-015-0816-y · Cited by 39,380
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Unpaired image-to-image translation using cycle-consistent adversarial networks
Zhu, J.-Y., Park, T., Isola, P., & Efros, A. A. (2017) · 2017 IEEE International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2017.244 · Cited by 23,602
Image-to-Image Translation with Conditional Adversarial Networks
Isola, P., Zhu, J.-Y., Zhou, T., & Efros, A. A. (2017) · 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2017.632 · Cited by 22,930
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Cited by
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
FMCNet: Feature-Level Modality Compensation for Visible-Infrared Person Re-Identification
Zhang, Q., Lai, C., Liu, J., Huang, N., & Han, J. (2022) · 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr52688.2022.00720 · Cited by 279
Fan, X., Jiang, W., Luo, H., & Mao, W. (2022) · The Visual Computer · doi:10.1007/s00371-020-02015-z · Cited by 45
Metadata
- Authors
- Wang, G., Zhang, T., Cheng, J., Liu, S., Yang, Y., & Hou, Z.
- Venue
- 2019 IEEE/CVF International Conference on Computer Vision (ICCV)
- Publisher
- IEEE
- Published
- 2019-10
- Volume
- 2019
- Pages
- 3622-3631
- OpenAlex
- W2985033611
- Cited by
- 559
- References
- 10
- FWCI
- 16.95
- Type
- conference paper
- Language
- en
- Open access
- green
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