  Classifying the sub-categories of an object from the same super-category
(e.g. bird species, car and aircraft models) in fine-grained visual
classification (FGVC) highly relies on discriminative feature representation
and accurate region localization. Existing approaches mainly focus on
distilling information from high-level features. In this paper, however, we
show that by integrating low-level information (e.g. color, edge junctions,
texture patterns), performance can be improved with enhanced feature
representation and accurately located discriminative regions. Our solution,
named Attention Pyramid Convolutional Neural Network (AP-CNN), consists of a) a
pyramidal hierarchy structure with a top-down feature pathway and a bottom-up
attention pathway, and hence learns both high-level semantic and low-level
detailed feature representation, and b) an ROI guided refinement strategy with
ROI guided dropblock and ROI guided zoom-in, which refines features with
discriminative local regions enhanced and background noises eliminated. The
proposed AP-CNN can be trained end-to-end, without the need of additional
bounding box/part annotations. Extensive experiments on three commonly used
FGVC datasets (CUB-200-2011, Stanford Cars, and FGVC-Aircraft) demonstrate that
our approach can achieve state-of-the-art performance. Code available at
\url{http://dwz1.cc/ci8so8a}

Computer Vision and Pattern Recognition