Improving Facial Attribute Prediction using Semantic Segmentation

Understand

Attributes are semantically meaningful characteristics whose applicability widely crosses category boundaries.

• They are particularly important in describing and recognizing concepts where no explicit training example is given, \textit{e.g., zero-shot learning}.
• Additionally, since attributes are human describable, they can be used for efficient human-computer interaction.
• In this paper, we propose to employ semantic segmentation to improve facial attribute prediction.