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.

本文提出一种新的深度学习方法，即分心感知的人体属性识别（Distraction-aware HAR），通过改进注意力机制来提高属性定位，从而提高深度CNN特征学习的准确性，在WIDER-Attribute和RAP数据库上进行广泛实验，并取得了最先进的结果。

通过粗到细的注意力机制，基于注意力特征学习，实现人体属性识别中的注意力分析