Joint Face Representation Adaptation and Clustering in Videos
Refereed conference paper presented and published in conference proceedings


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摘要Clustering faces in movies or videos is extremely challenging
since characters’ appearance can vary drastically under different scenes.
In addition, the various cinematic styles make it difficult to learn a
universal face representation for all videos. Unlike previous methods
that assume fixed handcrafted features for face clustering, in this work,
we formulate a joint face representation adaptation and clustering
approach in a deep learning framework. The proposed method allows
face representation to gradually adapt from an external source domain
to a target video domain. The adaptation of deep representation
is achieved without any strong supervision but through iteratively
discovered weak pairwise identity constraints derived from potentially
noisy face clustering result. Experiments on three benchmark video
datasets demonstrate that our approach generates character clusters with
high purity compared to existing video face clustering methods, which
are either based on deep face representation (without adaptation) or
carefully engineered features.
著者Zhanpeng Zhang, Ping Luo, Chen Change Loy, Xiaoou Tang
會議名稱The 14th European Conference on Computer Vision
會議開始日08.10.2016
會議完結日16.10.2016
會議地點Amsterdam
會議國家/地區荷蘭
會議論文集題名Computer Vision – ECCV 2016. Lecture Notes in Computer Science
出版年份2016
月份10
卷號9907
頁次236 - 251
國際標準書號978-3-319-46486-2
國際標準期刊號0302-9743
語言美式英語

上次更新時間 2020-07-08 於 02:15