The DOI matches its Crossref record; 43 of the 43 references it cites were found in a registry or an index.
Person Re-identification by Multi-Channel Parts-Based CNN with Improved Triplet Loss Function
Abstract
Person re-identification across cameras remains a very challenging problem, especially when there are no overlapping fields of view between cameras. In this paper, we present a novel multi-channel parts-based convolutional neural network (CNN) model under the triplet framework for person re-identification. Specifically, the proposed CNN model consists of multiple channels to jointly learn both the global full-body and local body-parts features of the input persons. The CNN model is trained by an improved triplet loss function that serves to pull the instances of the same person closer, and at the same time push the instances belonging to different persons farther from each other in the learned feature space. Extensive comparative evaluations demonstrate that our proposed method significantly outperforms many state-of-the-art approaches, including both traditional and deep network-based ones, on the challenging i-LIDS, VIPeR, PRID2011 and CUHK01 datasets.
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Cites
ImageNet classification with deep convolutional neural networks
Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2017) · Communications of the ACM · doi:10.1145/3065386 · Cited by 112,155
Rich Feature Hierarchies for Accurate Object Detection and Semantic Segmentation
Girshick, R., Donahue, J., Darrell, T., & Malik, J. (2014) · 2014 IEEE Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2014.81 · Cited by 32,684
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
Object Detection with Discriminatively Trained Part-Based Models
Felzenszwalb, P. F., Girshick, R. B., McAllester, D., & Ramanan, D. (2010) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2009.167 · Cited by 9,853
DeepPose: Human Pose Estimation via Deep Neural Networks
Toshev, A., & Szegedy, C. (2014) · 2014 IEEE Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2014.214 · Cited by 3,185
Distance Metric Learning with Application to Clustering with Side-Information
Xing, E. P., Jordan, M. I., Russell, S., & Ng, A. Y. (2002) · Cited by 2,700
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
Information-theoretic metric learning
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Cited by
A Survey of Convolutional Neural Networks: Analysis, Applications, and Prospects
Li, Z., Liu, F., Yang, W., Peng, S., & Zhou, J. (2022) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2021.3084827 · Cited by 5,273
“CNNs with triplet loss or its variants can be used in identification problems, such as face identification [71]–[73], person re-identification [74], [75], and vehicle re-identification [76].”
Person Transfer GAN to Bridge Domain Gap for Person Re-identification
Wei, L., Zhang, S., Gao, W., & Tian, Q. (2018) · 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2018.00016 · Cited by 2,118
Learning Discriminative Features with Multiple Granularities for Person Re-Identification
Wang, G., Yuan, Y., Chen, X., Li, J., & Zhou, X. (2018) · Proceedings of the 26th ACM international conference on Multimedia · doi:10.1145/3240508.3240552 · Cited by 1,203
Occlusion Aware Facial Expression Recognition Using CNN With Attention Mechanism
Li, Y., Zeng, J., Shan, S., & Chen, X. (2019) · IEEE Transactions on Image Processing · doi:10.1109/tip.2018.2886767 · Cited by 916
Joint Detection and Identification Feature Learning for Person Search
Xiao, T., Li, S., Wang, B., Lin, L., & Wang, X. (2017) · 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2017.360 · Cited by 890
Metadata
- Authors
- Cheng, D., Gong, Y., Zhou, S., Wang, J., & Zheng, N.
- Venue
- 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
- Publisher
- IEEE
- Published
- 2016-06
- Pages
- 1335-1344
- ISSN
- 1063-6919 (ISSN-L)
- OpenAlex
- W2467139031
- Cited by
- 1,247
- References
- 43
- FWCI
- 64.84
- Type
- conference paper
- Language
- en
- Open access
- closed
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- Cheng, D., Gong, Y., Zhou, S., Wang, J., & Zheng, N. (2016). Person Re-identification by Multi-Channel Parts-Based CNN with Improved Triplet Loss Function. 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). https://doi.org/10.1109/cvpr.2016.149
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