The DOI matches its Crossref record; 34 of the 34 references it cites were found in a registry or an index.
Deep Metric Learning with Hierarchical Triplet Loss
Open accessAbstract
We present a novel hierarchical triplet loss (HTL) capable of automatically collecting informative training samples (triplets) via a defined hierarchical tree that encodes global context information. This allows us to cope with the main limitation of random sampling in training a conventional triplet loss, which is a central issue for deep metric learning. Our main contributions are two-fold. (i) we construct a hierarchical class-level tree where neighboring classes are merged recursively. The hierarchical structure naturally captures the intrinsic data distribution over the whole dataset. (ii) we formulate the problem of triplet collection by introducing a new violate margin, which is computed dynamically based on the designed hierarchical tree. This allows it to automatically select meaningful hard samples with the guide of global context. It encourages the model to learn more discriminative features from visual similar classes, leading to faster convergence and better performance. Our method is evaluated on the tasks of image retrieval and face recognition, where it outperforms the standard triplet loss substantially by 1%–18%, and achieves new state-of-the-art performance on a number of benchmarks.
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Going deeper with convolutions
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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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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
Caffe: Convolutional Architecture for Fast Feature Embedding
Jia, Y., Shelhamer, E., Donahue, J., Karayev, S., Long, J., Girshick, R., Guadarrama, S., & Darrell, T. (2014) · Proceedings of the 22nd ACM international conference on Multimedia · doi:10.1145/2647868.2654889 · Cited by 11,077
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Dimensionality Reduction by Learning an Invariant Mapping
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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
A Discriminative Feature Learning Approach for Deep Face Recognition
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Cited by
Circle Loss: A Unified Perspective of Pair Similarity Optimization
Sun, Y., Cheng, C., Zhang, Y., Zhang, C., Zheng, L., Wang, Z., & Wei, Y. (2020) · 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr42600.2020.00643 · Cited by 987
Multi-Similarity Loss With General Pair Weighting for Deep Metric Learning
Wang, X., Han, X., Huang, W., Dong, D., & Scott, M. R. (2019) · 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2019.00516 · Cited by 854
Self-citationDeep Metric Learning: A Survey
KAYA, M., & BİLGE, H. Ş. (2019) · Symmetry · doi:10.3390/sym11091066 · Cited by 703
A Metric Learning Reality Check
Musgrave, K., Belongie, S., & Lim, S.-N. (2020) · Lecture Notes in Computer Science · doi:10.1007/978-3-030-58595-2_41 · Cited by 414
(2020) · doi:10.1007/978-3-030-58586-0_27 · Cited by 156
Metadata
- Authors
- Ge, W., Huang, W., Dong, D., & Scott, M. R.
- Venue
- Lecture Notes in Computer Science
- Publisher
- Springer International Publishing
- Published
- 2018
- Pages
- 272-288
- ISSN
- 0302-9743 (ISSN-L)
- 1611-3349
- OpenAlex
- W2895347732
- Cited by
- 445
- References
- 34
- FWCI
- 16.23
- Type
- conference paper
- Language
- en
- Open access
- green
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