  Recently, Human Attribute Recognition (HAR) has become a hot topic due to its
scientific challenges and application potentials, where localizing attributes
is a crucial stage but not well handled. In this paper, we propose a novel deep
learning approach to HAR, namely Distraction-aware HAR (Da-HAR). It enhances
deep CNN feature learning by improving attribute localization through a
coarse-to-fine attention mechanism. At the coarse step, a self-mask block is
built to roughly discriminate and reduce distractions, while at the fine step,
a masked attention branch is applied to further eliminate irrelevant regions.
Thanks to this mechanism, feature learning is more accurate, especially when
heavy occlusions and complex backgrounds exist. Extensive experiments are
conducted on the WIDER-Attribute and RAP databases, and state-of-the-art
results are achieved, demonstrating the effectiveness of the proposed approach.
