论文标题
Celeba属性值的一致性和准确性
Consistency and Accuracy of CelebA Attribute Values
论文作者
论文摘要
我们报告了面部属性分类实验基础的首次系统分析。独立分配属性值的两个注释者显示，在40个常见属性中只有12个具有> = 95％的一致性分配值，而三个（高chek骨，尖头的鼻子，椭圆面）基本上具有随机的一致性。在Celeba中的5,068次重复的面孔出现中，属性与5,068个重复项中的10到860个相矛盾。 Celeba子集的手动审核估计错误率（无胡须= false）高达40％，即使标签一致性实验表明，没有胡须可以分配> = 95％的一致性。选择嘴巴稍微开放（MSO）进行更深入的分析，我们估计（MSO = true）的错误率约为20％，（MSO = false）约为2％。 MSO属性值的校正版本使学习模型比以前报道的MSO更高的模型。可在https://github.com/haiyuwu/celebamso上获得Celeba MSO的校正值。
We report the first systematic analysis of the experimental foundations of facial attribute classification. Two annotators independently assigning attribute values shows that only 12 of 40 common attributes are assigned values with >= 95% consistency, and three (high cheekbones, pointed nose, oval face) have essentially random consistency. Of 5,068 duplicate face appearances in CelebA, attributes have contradicting values on from 10 to 860 of the 5,068 duplicates. Manual audit of a subset of CelebA estimates error rates as high as 40% for (no beard=false), even though the labeling consistency experiment indicates that no beard could be assigned with >= 95% consistency. Selecting the mouth slightly open (MSO) for deeper analysis, we estimate the error rate for (MSO=true) at about 20% and (MSO=false) at about 2%. A corrected version of the MSO attribute values enables learning a model that achieves higher accuracy than previously reported for MSO. Corrected values for CelebA MSO are available at https://github.com/HaiyuWu/CelebAMSO.
