Consensus-Driven Propagation in Massive Unlabeled Data for Face Recognition
Refereed conference paper presented and published in conference proceedings

替代計量分析
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其它資訊
摘要Face recognition has witnessed great progress in recent years, mainly attributed to the high-capacity model designed and the abundant labeled data collected. However, it becomes more and more prohibitive to scale up the current million-level identity annotations. In this work, we show that unlabeled face data can be as effective as the labeled ones. Here, we consider a setting closely mimicking the real-world scenario, where the unlabeled data are collected from unconstrained environments and their identities are exclusive from the labeled ones. Our main insight is that although the class information is not available, we can still faithfully approximate these semantic relationships by constructing a relational graph in a bottom-up manner. We propose Consensus-Driven Propagation (CDP) to tackle this challenging problem with two modules, the “committee” and the “mediator”, which select positive face pairs robustly by carefully aggregating multi-view information. Extensive experiments validate the effectiveness of both modules to discard outliers and mine hard positives. With CDP, we achieve a compelling accuracy of 78.18% on MegaFace identification challenge by using only 9% of the labels, comparing to 61.78% when no unlabeled data are used and 78.52% when all labels are employed.
出版社接受日期12.07.2018
著者Xiaohang Zhan, Ziwei Liu, Junjie Yan, Dahua Lin, Chen Change Loy
會議名稱15th European Conference on Computer Vision, ECCV 2018
會議開始日08.09.2018
會議完結日14.09.2018
會議地點Munich, Germany
會議國家/地區德國
會議論文集題名Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
出版年份2018
月份9
卷號11213
出版社Springer
頁次576 - 592
國際標準書號978-303001239-7
國際標準期刊號03029743
語言美式英語

上次更新時間 2021-12-01 於 01:15