The DOI matches its Crossref record; 44 of the 44 references it cites were found in a registry or an index.
Sampling Matters in Deep Embedding Learning
Abstract
Deep embeddings answer one simple question: How similar are two images? Learning these embeddings is the bedrock of verification, zero-shot learning, and visual search. The most prominent approaches optimize a deep convolutional network with a suitable loss function, such as contrastive loss or triplet loss. While a rich line of work focuses solely on the loss functions, we show in this paper that selecting training examples plays an equally important role. We propose distance weighted sampling, which selects more informative and stable examples than traditional approaches. In addition, we show that a simple margin based loss is sufficient to outperform all other loss functions. We evaluate our approach on the Stanford Online Products, CAR196, and the CUB200-2011 datasets for image retrieval and clustering, and on the LFW dataset for face verification. Our method achieves state-of-the-art performance on all of them.
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Cites
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Cited by
ArcFace: Additive Angular Margin Loss for Deep Face Recognition
Deng, J., Guo, J., Xue, N., & Zafeiriou, S. (2019) · 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2019.00482 · Cited by 7,781
Bootstrap Your Own Latent: A New Approach to Self-Supervised Learning
Grill, J.-B., Strub, F., Altché, F., Tallec, C., Richemond, P. H., Buchatskaya, E., Doersch, C., Pires, B. A., Guo, Z. D., Azar, M. G., et al. (2020) · arXiv · doi:10.48550/arxiv.2006.07733 · Cited by 3,846
Contrastive Representation Learning: A Framework and Review
Le-Khac, P. H., Healy, G., & Smeaton, A. F. (2020) · IEEE Access · doi:10.1109/access.2020.3031549 · Cited by 862
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
Unsupervised Embedding Learning via Invariant and Spreading Instance Feature
Ye, M., Zhang, X., Yuen, P. C., & Chang, S.-F. (2019) · 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2019.00637 · Cited by 601
Metadata
- Authors
- Wu, C.-Y., Manmatha, R., Smola, A. J., & Krahenbuhl, P.
- Venue
- 2017 IEEE International Conference on Computer Vision (ICCV)
- Publisher
- IEEE
- Published
- 2017-10
- Pages
- 2859-2867
- OpenAlex
- W2963350250
- Cited by
- 836
- References
- 44
- FWCI
- 24.83
- Type
- conference paper
- Language
- en
- Open access
- closed
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- Wu, C.-Y., Manmatha, R., Smola, A. J., & Krahenbuhl, P. (2017). Sampling Matters in Deep Embedding Learning. 2017 IEEE International Conference on Computer Vision (ICCV). https://doi.org/10.1109/iccv.2017.309
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