The DOI matches its Crossref record; 37 of the 37 references it cites were found in a registry or an index.
Quality Aware Network for Set to Set Recognition
Open accessAbstract
This paper targets on the problem of set to set recognition, which learns the metric between two image sets. Images in each set belong to the same identity. Since images in a set can be complementary, they hopefully lead to higher accuracy in practical applications. However, the quality of each sample cannot be guaranteed, and samples with poor quality will hurt the metric. In this paper, the quality aware network (QAN) is proposed to confront this problem, where the quality of each sample can be automatically learned although such information is not explicitly provided in the training stage. The network has two branches, where the first branch extracts appearance feature embedding for each sample and the other branch predicts quality score for each sample. Features and quality scores of all samples in a set are then aggregated to generate the final feature embedding. We show that the two branches can be trained in an end-to-end manner given only the set-level identity annotation. Analysis on gradient spread of this mechanism indicates that the quality learned by the network is beneficial to set-to-set recognition and simplifies the distribution that the network needs to fit. Experiments on both face verification and person re-identification show advantages of the proposed QAN. The source code and network structure can be downloaded at GitHub.
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Cites
FaceNet: A unified embedding for face recognition and clustering
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Beyond Bags of Features: Spatial Pyramid Matching for Recognizing Natural Scene Categories
Lazebnik, S., Schmid, C., & Ponce, J. (2006) · 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 2 (CVPR'06) · doi:10.1109/cvpr.2006.68 · Cited by 7,916
DeepFace: Closing the Gap to Human-Level Performance in Face Verification
Taigman, Y., Yang, M., Ranzato, M., & Wolf, L. (2014) · 2014 IEEE Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2014.220 · Cited by 6,542
Parkhi, O. M., Vedaldi, A., & Zisserman, A. (2015) · Procedings of the British Machine Vision Conference 2015 · doi:10.5244/c.29.41 · Cited by 5,479
Labeled Faces in the Wild: A Database forStudying Face Recognition in Unconstrained Environments
Huang, G. B., Mattar, M., Berg, T. L., & Miller, E. G. (2008) · HAL · Cited by 4,445
Aggregating local descriptors into a compact image representation
Jegou, H., Douze, M., Schmid, C., & Perez, P. (2010) · 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2010.5540039 · Cited by 2,692
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
Deep Learning Face Representation from Predicting 10,000 Classes
Sun, Y., Wang, X., & Tang, X. (2014) · 2014 IEEE Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2014.244 · Cited by 1,997
Deep Learning Face Representation by Joint Identification-Verification
Sun, Y., Wang, X., & Tang, X. (2014) · arXiv · doi:10.48550/arxiv.1406.4773 · Cited by 1,906
Large scale metric learning from equivalence constraints
Kostinger, M., Hirzer, M., Wohlhart, P., Roth, P. M., & Bischof, H. (2012) · 2012 IEEE Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2012.6247939 · Cited by 1,606
Cited by
Towards Real-Time Multi-Object Tracking
Wang, Z., Zheng, L., Liu, Y., Li, Y., & Wang, S. (2020) · Lecture Notes in Computer Science · doi:10.1007/978-3-030-58621-8_7 · Cited by 1,195
Self-citationA survey on deep learning based face recognition
Guo, G., & Zhang, N. (2019) · Computer Vision and Image Understanding · doi:10.1016/j.cviu.2019.102805 · Cited by 485
Dual Attention Matching Network for Context-Aware Feature Sequence Based Person Re-identification
Si, J., Zhang, H., Li, C.-G., Kuen, J., Kong, X., Kot, A. C., & Wang, G. (2018) · 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2018.00562 · Cited by 466
AlignedReID: Surpassing Human-Level Performance in Person Re-Identification
Zhang, X., Luo, H., Fan, X., Xiang, W., Sun, Y., Xiao, Q., Jiang, W., Zhang, C., & Sun, J. (2017) · arXiv · doi:10.48550/arxiv.1711.08184 · Cited by 419
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
Metadata
- Authors
- Liu, Y., Yan, J., & Ouyang, W.
- Venue
- 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
- Publisher
- IEEE
- Published
- 2017-07
- Pages
- 4694-4703
- ISSN
- 1063-6919 (ISSN-L)
- OpenAlex
- W2963216120
- Cited by
- 339
- References
- 37
- FWCI
- 15.61
- Type
- conference paper
- Language
- en
- Open access
- green
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