论文标题
扩展标签的面孔（ELFW）：面部分割的增强课程
Extended Labeled Faces in-the-Wild (ELFW): Augmenting Classes for Face Segmentation
论文作者
论文摘要
现有的面部数据集通常缺乏充分的阻塞对象的表示，这可能会阻碍识别，但也提供有意义的信息来理解视觉上下文。在这项工作中，我们介绍了野外标记的扩展面孔（ELFW），该数据集补充了其他与面部有关的类别（以及其他面孔），是最初发布的语义标签，该标签最初发布了贴有贴标签（LFW）数据集中的标签面孔。此外，还部署了两种基于对象的数据增强技术来合成富集代表性不足的类别，这些类别在基准测试实验中表明，不仅分割增强类别的细分会有所改善，而且其余的则受益。
Existing face datasets often lack sufficient representation of occluding objects, which can hinder recognition, but also supply meaningful information to understand the visual context. In this work, we introduce Extended Labeled Faces in-the-Wild (ELFW), a dataset supplementing with additional face-related categories -- and also additional faces -- the originally released semantic labels in the vastly used Labeled Faces in-the-Wild (LFW) dataset. Additionally, two object-based data augmentation techniques are deployed to synthetically enrich under-represented categories which, in benchmarking experiments, reveal that not only segmenting the augmented categories improves, but also the remaining ones benefit.
