The DOI matches its Crossref record; 43 of the 43 references it cites were found in a registry or an index.
Multi-Similarity Loss With General Pair Weighting for Deep Metric Learning
Open accessAbstract
A family of loss functions built on pair-based computation have been proposed in the literature which provide a myriad of solutions for deep metric learning. In this pa-per, we provide a general weighting framework for under-standing recent pair-based loss functions. Our contributions are three-fold: (1) we establish a General Pair Weighting (GPW) framework, which casts the sampling problem of deep metric learning into a unified view of pair weighting through gradient analysis, providing a powerful tool for understanding recent pair-based loss functions; (2) we show that with GPW, various existing pair-based methods can be compared and discussed comprehensively, with clear differences and key limitations identified; (3) we propose a new loss called multi-similarity loss (MS loss) under the GPW,which is implemented in two iterative steps (i.e., mining and weighting). This allows it to fully consider three similarities for pair weighting, providing a more principled approach for collecting and weighting informative pairs. Finally, the proposed MS loss obtains new state-of-the-art performance on four image retrieval benchmarks, where it outperforms the most recent approaches, such as ABE[14] and HTL[4], by a large margin, e.g.,60.6%→65.7%on CUB200,and 80.9%→88.0%on In-Shop Clothes Retrieval datasetat Recall@1.
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Deep Metric Learning: A Survey
KAYA, M., & BİLGE, H. Ş. (2019) · Symmetry · doi:10.3390/sym11091066 · Cited by 703
Semantic Drift Compensation for Class-Incremental Learning
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Detecting Deep-Fake Videos from Appearance and Behavior
Agarwal, S., Farid, H., El-Gaaly, T., & Lim, S.-N. (2020) · 2020 IEEE International Workshop on Information Forensics and Security (WIFS) · doi:10.1109/wifs49906.2020.9360904 · Cited by 242
“All other optimization hyper-parameters are the same as in [33].”
BackgroundMethodInfluentialID-Reveal: Identity-aware DeepFake Video Detection
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Ad-Corre: Adaptive Correlation-Based Loss for Facial Expression Recognition in the Wild
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Metadata
- Authors
- Wang, X., Han, X., Huang, W., Dong, D., & Scott, M. R.
- Venue
- 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
- Publisher
- IEEE
- Published
- 2019-06
- Pages
- 5017-5025
- ISSN
- 1063-6919 (ISSN-L)
- OpenAlex
- W2964271799
- Cited by
- 854
- References
- 43
- FWCI
- 35.17
- Type
- conference paper
- Language
- en
- Open access
- green
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