  Pedestrian attribute recognition in surveillance scenarios is still a
challenging task due to the inaccurate localization of specific attributes. In
this paper, we propose a novel view-attribute localization method based on
attention (VALA), which utilizes view information to guide the recognition
process to focus on specific attributes and attention mechanism to localize
specific attribute-corresponding areas. Concretely, view information is
leveraged by the view prediction branch to generate four view weights that
represent the confidences for attributes from different views. View weights are
then delivered back to compose specific view-attributes, which will participate
and supervise deep feature extraction. In order to explore the spatial location
of a view-attribute, regional attention is introduced to aggregate spatial
information and encode inter-channel dependencies of the view feature.
Subsequently, a fine attentive attribute-specific region is localized, and
regional weights for the view-attribute from different spatial locations are
gained by the regional attention. The final view-attribute recognition outcome
is obtained by combining the view weights with the regional weights.
Experiments on three wide datasets (RAP, RAPv2, and PA-100K) demonstrate the
effectiveness of our approach compared with state-of-the-art methods.
