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Learning to Cluster Faces via Confidence and Connectivity Estimation
[article]
2020
arXiv
pre-print
Face clustering is an essential tool for exploiting the unlabeled face data, and has a wide range of applications including face annotation and retrieval. Recent works show that supervised clustering can result in noticeable performance gain. However, they usually involve heuristic steps and require numerous overlapped subgraphs, severely restricting their accuracy and efficiency. In this paper, we propose a fully learnable clustering framework without requiring a large number of overlapped
arXiv:2004.00445v2
fatcat:r7g6wywjjrhwbnl7hhj3xos2zi