  Fine-grained visual classification (FGVC) is challenging but more critical
than traditional classification tasks. It requires distinguishing different
subcategories with the inherently subtle intra-class object variations.
Previous works focus on enhancing the feature representation ability using
multiple granularities and discriminative regions based on the attention
strategy or bounding boxes. However, these methods highly rely on deep neural
networks which lack interpretability. We propose an Interpretable Attention
Guided Network (IAGN) for fine-grained visual classification. The contributions
of our method include: i) an attention guided framework which can guide the
network to extract discriminitive regions in an interpretable way; ii) a
progressive training mechanism obtained to distill knowledge stage by stage to
fuse features of various granularities; iii) the first interpretable FGVC
method with a competitive performance on several standard FGVC benchmark
datasets.

Computer Vision and Pattern Recognition