论文标题
通过共同学习使用亲属验证合奏来识别亲属识别
Kinship Identification through Joint Learning Using Kinship Verification Ensembles
论文作者
论文摘要
亲属验证是一项精心探索的任务：确定两个人是否是亲属。相比之下，到目前为止，亲属识别已在很大程度上被忽略了。亲属识别旨在进一步确定特定类型的亲属关系。亲属验证的扩展空缺以适当获得识别，因为现有的验证网络是对特定亲属关系进行单独培训的，并且不考虑不同亲属类型之间的上下文。此外，现有的亲属验证数据集具有与现实世界分布不同的阳性阴性分布。为此，我们提出了一种基于亲属验证合奏和分类模块的联合培训的新型亲属识别方法。我们建议重新平衡培训数据集，以变得更加现实。大规模实验证明了亲属识别方面的吸引力。实验进一步显示了在具有更现实的分布的同一数据集上训练的亲属验证的显着改善。
Kinship verification is a well-explored task: identifying whether or not two persons are kin. In contrast, kinship identification has been largely ignored so far. Kinship identification aims to further identify the particular type of kinship. An extension to kinship verification run short to properly obtain identification, because existing verification networks are individually trained on specific kinships and do not consider the context between different kinship types. Also, existing kinship verification datasets have biased positive-negative distributions which are different than real-world distributions. To this end, we propose a novel kinship identification approach based on joint training of kinship verification ensembles and classification modules. We propose to rebalance the training dataset to become more realistic. Large scale experiments demonstrate the appealing performance on kinship identification. The experiments further show significant performance improvement of kinship verification when trained on the same dataset with more realistic distributions.
