HIGHLIGHTS
- who: Shaoying Li from the Hebei Agricultural University, College of Information Science and Technology, Baoding, Hebei, China have published the paper: Structure-enhanced pairwise feature learning for face clustering, in the Journal: (JOURNAL)
- what: Specifically the authors propose a novel framework named structure-enhanced feature learning (SEPFL) which mixes information to adaptively produce representations for cluster identification. The authors design a combined strategy to select representative pairs thus ensuring training effectiveness and inference efficiency. The authors propose a novel face clustering framework that performs data grouping at the pair level. The authors propose a neighborhood . . .
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