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.
In @cite_10 , the proposed network has convolution layers followed by a global max-pooling layer. Each channel of the global max-pooling layer is viewed as a detector for a certain class of object. It is assumed that the positions of max value point in the feature map correspond to the locations where the objects of the target class exist in. However, this method cannot be directly applied to our attribute localization task. Firstly, different from objects, some attributes are abstract concepts, such as gender, orientation and age, which do not correspond to certain regions. Secondly, some attributes such as hat wearing or shoe style are expected to appear within a certain partition in a pedestrian sample, which can be used to improve the localization of those attributes. Thus, to better fit the task of attribute localization, we embed this structure in the middle stage of the network to discover mid-level features relevant to attributes rather than attributes themselves, and propose to use FSPP layers instead of a single global max-pooling layer to help constraining location of certain attributes.
@cite_10
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Successful methods for visual object recognition typically rely on training datasets containing lots of richly annotated images. Detailed image annotation, e.g. by object bounding boxes, however, is both expensive and often subjective. We describe a weakly supervised convolutional neural network (CNN) for object classification that relies only on image-level labels, yet can learn from cluttered scenes containing multiple objects. We quantify its object classification and object location prediction performance on the Pascal VOC 2012 (20 object classes) and the much larger Microsoft COCO (80 object classes) datasets. We find that the network (i) outputs accurate image-level labels, (ii) predicts approximate locations (but not extents) of objects, and (iii) performs comparably to its fully-supervised counterparts using object bounding box annotation for training.