Papers › Improving Facial Attribute Prediction using Semantic Segmentation

Improving Facial Attribute Prediction using Semantic Segmentation

27 Apr 2017CVPR 2017 7arXiv:1704.08740archive 2025-07-28

Mahdi M. Kalayeh, Boqing Gong, Mubarak Shah

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. The core idea lies in the fact that many facial attributes describe local properties. In other words, the probability of an attribute to appear in a face image is far from being uniform in the spatial domain. We build our facial attribute prediction model jointly with a deep semantic segmentation network. This harnesses the localization cues learned by the semantic segmentation to guide the attention of the attribute prediction to the regions where different attributes naturally show up. As a result of this approach, in addition to recognition, we are able to localize the attributes, despite merely having access to image level labels (weak supervision) during training. We evaluate our proposed method on CelebA and LFWA datasets and achieve superior results to the prior arts. Furthermore, we show that in the reverse problem, semantic face parsing improves when facial attributes are available. That reaffirms the need to jointly model these two interconnected tasks.

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Tasks

Attribute Face Parsing Facial Attribute Classification Prediction Segmentation Semantic Segmentation Zero-Shot Learning

Results from the paper archive 2025-07-28

Task	Dataset	Model	Metric	Value	Rank at snapshot	Leaderboard	Report
Facial Attribute Classification	LFWA	SSP + SSG	Error Rate	12.87	#2 of 7	Archive leaderboard	report

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