Joint Face Representation Adaptation and Clustering in Videos
文献类型：会议论文
| 作者 | Zhanpeng Zhang; Ping Luo; Chen Change Loy; Xiaoou Tang |
| 出版日期 | 2016 |
| 会议名称 | ECCV2016 |
| 会议地点 | 荷兰阿姆斯特丹 |
| 英文摘要 | Clustering faces in movies or videos is extremely challenging since characters' appearance can vary drastically under di erent scenes. In addition, the various cinematic styles make it di cult to learn a universal face representation for all videos. Unlike previous methods that assume xed 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. |
| 收录类别 | EI |
| 语种 | 英语 |
| 源URL | [http://ir.siat.ac.cn:8080/handle/172644/10015]  |
| 专题 | 深圳先进技术研究院_集成所 |
| 作者单位 | 2016 |
| 推荐引用方式 GB/T 7714 | Zhanpeng Zhang,Ping Luo,Chen Change Loy,et al. Joint Face Representation Adaptation and Clustering in Videos[C]. 见:ECCV2016. 荷兰阿姆斯特丹. |
入库方式： OAI收割
来源：深圳先进技术研究院
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