摘要
Associating faces appearing in Web videos with names presented in the surrounding context is an important task in many applications. However, the problem is not well investigated particularly under large-scale realistic scenario,mainly due to the scarcity of dataset constructed in such circumstance. In this paper, we introduce a Web video dataset of celebrities, named WebV-Cele, for name-face association. The dataset consists of 75 073 Internet videos of over 4 000 hours,covering 2 427 celebrities and 649 001 faces. This is, to our knowledge, the most comprehensive dataset for this problem.We describe the details of dataset construction, discuss several interesting findings by analyzing this dataset like celebrity community discovery, and provide experimental results of name-face association using five existing techniques. We also outline important and challenging research problems that could be investigated in the future.
Associating faces appearing in Web videos with names presented in the surrounding context is an important task in many applications. However, the problem is not well investigated particularly under large-scale realistic scenario,mainly due to the scarcity of dataset constructed in such circumstance. In this paper, we introduce a Web video dataset of celebrities, named WebV-Cele, for name-face association. The dataset consists of 75 073 Internet videos of over 4 000 hours,covering 2 427 celebrities and 649 001 faces. This is, to our knowledge, the most comprehensive dataset for this problem.We describe the details of dataset construction, discuss several interesting findings by analyzing this dataset like celebrity community discovery, and provide experimental results of name-face association using five existing techniques. We also outline important and challenging research problems that could be investigated in the future.
基金
supported by a research grant from City University of Hong Kong under Grant No.7008178
the National Natural Science Foundation of China under Grant Nos.61228205,61303175 and 61172153
