  How to learn a discriminative fine-grained representation is a key point in
many computer vision applications, such as person re-identification,
fine-grained classification, fine-grained image retrieval, etc. Most of the
previous methods focus on learning metrics or ensemble to derive better global
representation, which are usually lack of local information. Based on the
considerations above, we propose a novel Attribute-Aware Attention Model
($A^3M$), which can learn local attribute representation and global category
representation simultaneously in an end-to-end manner. The proposed model
contains two attention models: attribute-guided attention module uses attribute
information to help select category features in different regions, at the same
time, category-guided attention module selects local features of different
attributes with the help of category cues. Through this attribute-category
reciprocal process, local and global features benefit from each other. Finally,
the resulting feature contains more intrinsic information for image recognition
instead of the noisy and irrelevant features. Extensive experiments conducted
on Market-1501, CompCars, CUB-200-2011 and CARS196 demonstrate the
effectiveness of our $A^3M$. Code is available at
https://github.com/iamhankai/attribute-aware-attention.
