State-of-the-art methods treat pedestrian attribute recognition as a
multi-label image classification problem. The location information of person
attributes is usually eliminated or simply encoded in the rigid splitting of
whole body in previous work. In this paper, we formulate the task in a
weakly-supervised attribute localization framework. Based on GoogLeNet,
firstly, a set of mid-level attribute features are discovered by novelly
designed detection layers, where a max-pooling based weakly-supervised object
detection technique is used to train these layers with only image-level labels
without the need of bounding box annotations of pedestrian attributes.
Secondly, attribute labels are predicted by regression of the detection
response magnitudes. Finally, the locations and rough shapes of pedestrian
attributes can be inferred by performing clustering on a fusion of activation
maps of the detection layers, where the fusion weights are estimated as the
correlation strengths between each attribute and its relevant mid-level
features. Extensive experiments are performed on the two currently largest
pedestrian attribute datasets, i.e. the PETA dataset and the RAP dataset.
Results show that the proposed method has achieved competitive performance on
attribute recognition, compared to other state-of-the-art methods. Moreover,
the results of attribute localization are visualized to understand the
characteristics of the proposed method.

本文提出了一种基于 GoogLeNet 网络的弱监督属性定位框架来解决行人属性识别问题，其中使用最大池化的弱监督目标检测技术来训练属性特征图，并通过检测响应大小的回归来预测属性标签，通过对检测层激活图的融合加权聚类，可以推断出行人属性的位置和大致形状。实验证明该方法在行人属性识别和定位方面取得了有竞争力的表现。