学习的准传递相似性用于从大量人脸集合中检索
Learnt quasi-transitive similarity for retrieval from large collections of faces
摘要：
我们感兴趣的是在不受控制的环境中获得的大型未标记的集合的人脸集的基于身份的检索。给定用于测量两个人脸集的相似性的基线算法，本文中介绍的元算法试图利用数据语料库的结构来最好地利用可用的基线。特别是，我们展示了如何利用人际相似性的部分传递性来提高检索特别具有挑战性的集合，这些集合在基线测量下与查询匹配得很差。我方：（i）描述使用代理集作为计算两个集之间的相似性的手段，（ii）基于集之间的外观变化的突出模式的相似性引入传递性元特征，（iii）示出如何在没有任何标记或人工干预的情况下从这些特征学习准传递性，和（iv）通过在众所周知的具有挑战性的YouTube数据库和文献中的两个成功基线上的实验来证明所提出的方法的有效性。
英文摘要：
We are interested in identity-based retrieval of face sets from large unlabelled collections acquired in uncontrolled environments. Given a baseline algorithm for measuring the similarity of two face sets, the meta-algorithm introduced in this paper seeks to leverage the structure of the data corpus to make the best use of the available baseline. In particular, we show how partial transitivity of inter-personal similarity can be exploited to improve the retrieval of particularly challenging sets which poorly match the query under the baseline measure. We: (i) describe the use of proxy sets as a means of computing the similarity between two sets, (ii) introduce transitivity meta-features based on the similarity of salient modes of appearance variation between sets, (iii) show how quasi-transitivity can be learnt from such features without any labelling or manual intervention, and (iv) demonstrate the effectiveness of the proposed methodology through experiments on the notoriously challenging YouTube database and two successful baselines from the literature.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
