Pairwise Similarity Learning is SimPLE

Yandong Wen, Weiyang Liu, Yao Feng, Bhiksha Raj, Rita Singh, Adrian Weller, Michael J. Black, Bernhard Schölkopf; Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), 2023, pp. 5308-5318


In this paper, we focus on a general yet important learning problem, pairwise similarity learning (PSL). PSL subsumes a wide range of important applications, such as open-set face recognition, speaker verification, image retrieval and person re-identification. The goal of PSL is to learn a pairwise similarity function assigning a higher similarity score to positive pairs (i.e., a pair of samples with the same label) than to negative pairs (i.e., a pair of samples with different label). We start by identifying a key desideratum for PSL, and then discuss how existing methods can achieve this desideratum. We then propose a surprisingly simple proxy-free method, called SimPLE, which requires neither feature/proxy normalization nor angular margin and yet is able to generalize well in open-set recognition. We apply the proposed method to three challenging PSL tasks: open-set face recognition, image retrieval and speaker verification. Comprehensive experimental results on large-scale benchmarks show that our method performs significantly better than current state-of-the-art methods.

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@InProceedings{Wen_2023_ICCV, author = {Wen, Yandong and Liu, Weiyang and Feng, Yao and Raj, Bhiksha and Singh, Rita and Weller, Adrian and Black, Michael J. and Sch\"olkopf, Bernhard}, title = {Pairwise Similarity Learning is SimPLE}, booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)}, month = {October}, year = {2023}, pages = {5308-5318} }