  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.
