Weakly-supervised Learning of Mid-level Features for Pedestrian Attribute Recognition and Localization

Understand

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.