Pedestrian attribute recognition has attracted many attentions due to its
wide applications in scene understanding and person analysis from surveillance
videos. Existing methods try to use additional pose, part or viewpoint
information to complement the global feature representation for attribute
classification. However, these methods face difficulties in localizing the
areas corresponding to different attributes. To address this problem, we
propose a novel Localization Guided Network which assigns attribute-specific
weights to local features based on the affinity between proposals pre-extracted
proposals and attribute locations. The advantage of our model is that our local
features are learned automatically for each attribute and emphasized by the
interaction with global features. We demonstrate the effectiveness of our
Localization Guided Network on two pedestrian attribute benchmarks (PA-100K and
RAP). Our result surpasses the previous state-of-the-art in all five metrics on
both datasets.

提出了一种基于定位的引导网络，该网络为每个属性自动学习本地特征，并通过与全局特征的互动强调它们，在两个行人属性基准测试中取得了最新的最佳成绩。