The DOI matches its Crossref record; 65 of the 65 references it cites were found in a registry or an index.
Dual Attention Matching Network for Context-Aware Feature Sequence Based Person Re-identification
Abstract
Typical person re-identification (ReID) methods usually describe each pedestrian with a single feature vector and match them in a task-specific metric space. However, the methods based on a single feature vector are not sufficient enough to overcome visual ambiguity, which frequently occurs in real scenario. In this paper, we propose a novel end-to-end trainable framework, called Dual ATtention Matching network (DuATM), to learn context-aware feature sequences and perform attentive sequence comparison simultaneously. The core component of our DuATM framework is a dual attention mechanism, in which both intrasequence and inter-sequence attention strategies are used for feature refinement and feature-pair alignment, respectively. Thus, detailed visual cues contained in the intermediate feature sequences can be automatically exploited and properly compared. We train the proposed DuATM network as a siamese network via a triplet loss assisted with a decorrelation loss and a cross-entropy loss. We conduct extensive experiments on both image and video based ReID benchmark datasets. Experimental results demonstrate the significant advantages of our approach compared to the state-of-the-art methods.
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Densely Connected Convolutional Networks
Huang, G., Liu, Z., Van Der Maaten, L., & Weinberger, K. Q. (2017) · 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2017.243 · Cited by 48,566
Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift
Ioffe, S., & Szegedy, C. (2015) · arXiv · doi:10.48550/arxiv.1502.03167 · Cited by 27,818
FaceNet: A unified embedding for face recognition and clustering
Schroff, F., Kalenichenko, D., & Philbin, J. (2015) · 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2015.7298682 · Cited by 15,267
Show, Attend and Tell: Neural Image Caption Generation with Visual Attention
Xu, K., Ba, J., Kiros, R., Cho, K., Courville, A., Salakhutdinov, R., Zemel, R., & Bengio, Y. (2015) · arXiv · doi:10.48550/arxiv.1502.03044 · Cited by 8,248
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
Random Erasing Data Augmentation
Zhong, Z., Zheng, L., Kang, G., Li, S., & Yang, Y. (2020) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v34i07.7000 · Cited by 3,617
Performance Measures and a Data Set for Multi-target, Multi-camera Tracking
Ristani, E., Solera, F., Zou, R., Cucchiara, R., & Tomasi, C. (2016) · Lecture Notes in Computer Science · doi:10.1007/978-3-319-48881-3_2 · Cited by 2,914
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
DeepReID: Deep Filter Pairing Neural Network for Person Re-identification
Li, W., Zhao, R., Xiao, T., & Wang, X. (2014) · 2014 IEEE Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2014.27 · Cited by 2,611
Person re-identification by Local Maximal Occurrence representation and metric learning
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Cited by
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
ABD-Net: Attentive but Diverse Person Re-Identification
Chen, T., Ding, S., Xie, J., Yuan, Y., Chen, W., Yang, Y., Ren, Z., & Wang, Z. (2019) · 2019 IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2019.00844 · Cited by 561
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
Counterfactual Attention Learning for Fine-Grained Visual Categorization and Re-identification
Rao, Y., Chen, G., Lu, J., & Zhou, J. (2021) · 2021 IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv48922.2021.00106 · Cited by 380
Attention, please! A survey of neural attention models in deep learning
de Santana Correia, A., & Colombini, E. L. (2022) · Artificial Intelligence Review · doi:10.1007/s10462-022-10148-x · Cited by 298
Metadata
- Authors
- Si, J., Zhang, H., Li, C.-G., Kuen, J., Kong, X., Kot, A. C., & Wang, G.
- Venue
- 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition
- Publisher
- IEEE
- Published
- 2018-06
- Pages
- 5363-5372
- ISSN
- 1063-6919 (ISSN-L)
- OpenAlex
- W2964044605
- Cited by
- 466
- References
- 65
- FWCI
- 22.46
- Type
- conference paper
- Language
- en
- Open access
- closed
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- Si, J., Zhang, H., Li, C.-G., Kuen, J., Kong, X., Kot, A. C., & Wang, G. (2018). Dual Attention Matching Network for Context-Aware Feature Sequence Based Person Re-identification. 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition. https://doi.org/10.1109/cvpr.2018.00562
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