The DOI matches its Crossref record; 70 of the 70 references it cites were found in a registry or an index.
Visible-Infrared Person Re-Identification via Homogeneous Augmented Tri-Modal Learning
Abstract
Matching person images between the daytime visible modality and night-time infrared modality (VI-ReID) is a challenging cross-modality pedestrian retrieval problem. Existing methods usually learn the multi-modality features in raw image, ignoring the image-level discrepancy. Some methods apply GAN technique to generate the cross-modality images, but it destroys the local structure and introduces unavoidable noise. In this paper, we propose a Homogeneous Augmented Tri-Modal (HAT) learning method for VI-ReID, where an auxiliary grayscale modality is generated from their homogeneous visible images, without additional training process. It preserves the structure information of visible images and approximates the image style of infrared modality. Learning with the grayscale visible images enforces the network to mine structure relations across multiple modalities, making it robust to color variations. Specifically, we solve the tri-modal feature learning from both multi-modal classification and multi-view retrieval perspectives. For multi-modal classification, we learn a multi-modality sharing identity classifier with a parameter-sharing network, trained with a homogeneous and heterogeneous identification loss. For multi-view retrieval, we develop a weighted tri-directional ranking loss to optimize the relative distance across multiple modalities. Incorporated with two invariant regularizers, HAT simultaneously minimizes multiple modality variations. In-depth analysis demonstrates the homogeneous grayscale augmentation significantly outperforms the current state-of-the-art by a large margin.
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Cites
Deep Residual Learning for Image Recognition
He, K., Zhang, X., Ren, S., & Sun, J. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.90 · Cited by 235,474
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
In Defense of the Triplet Loss for Person Re-Identification
Hermans, A., Beyer, L., & Leibe, B. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1703.07737 · Cited by 2,687
Deep Learning for Person Re-Identification: A Survey and Outlook
Ye, M., Shen, J., Lin, G., Xiang, T., Shao, L., & Hoi, S. C. H. (2022) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2021.3054775 · Cited by 2,259
Self-citationPerson 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
Unlabeled Samples Generated by GAN Improve the Person Re-identification Baseline in Vitro
Zheng, Z., Zheng, L., & Yang, Y. (2017) · 2017 IEEE International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2017.405 · Cited by 1,984
Deep Metric Learning via Lifted Structured Feature Embedding
Song, H. O., Xiang, Y., Jegelka, S., & Savarese, S. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.434 · Cited by 1,630
Deng, W., Zheng, L., Ye, Q., Kang, G., Yang, Y., & Jiao, J. (2018) · 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2018.00110 · Cited by 1,011
MARS: A Video Benchmark for Large-Scale Person Re-Identification
Zheng, L., Bie, Z., Sun, Y., Wang, J., Su, C., Wang, S., & Tian, Q. (2016) · Lecture Notes in Computer Science · doi:10.1007/978-3-319-46466-4_52 · Cited by 965
Deep Metric Learning for Person Re-identification
Yi, D., Lei, Z., Liao, S., & Li, S. Z. (2014) · 2014 22nd International Conference on Pattern Recognition · doi:10.1109/icpr.2014.16 · Cited by 931
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
Self-citationChannel 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
Self-citationFMCNet: 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
Neural Feature Search for RGB-Infrared Person Re-Identification
Chen, Y., Wan, L., Li, Z., Jing, Q., & Sun, Z. (2021) · 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr46437.2021.00065 · Cited by 253
Metadata
- Authors
- Ye, M., Shen, J., & Shao, L.
- Venue
- IEEE Transactions on Information Forensics and Security
- Published
- 2021
- Volume
- 16
- Pages
- 728-739
- ISSN
- 1556-6013 (ISSN-L)
- 1556-6021
- OpenAlex
- W3035673257
- Cited by
- 305
- References
- 70
- FWCI
- 17.33
- Type
- journal article
- Language
- en
- Open access
- closed
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