  The term fine-grained visual classification (FGVC) refers to classification
tasks where the classes are very similar and the classification model needs to
be able to find subtle differences to make the correct prediction.
State-of-the-art approaches often include a localization step designed to help
a classification network by localizing the relevant parts of the input images.
However, this usually requires multiple iterations or passes through a full
classification network or complex training schedules. In this work we present
an efficient localization module that can be fused with a classification
network in an end-to-end setup. On the one hand the module is trained by the
gradient flowing back from the classification network. On the other hand, two
self-supervised loss functions are introduced to increase the localization
accuracy. We evaluate the new model on the three benchmark datasets
CUB200-2011, Stanford Cars and FGVC-Aircraft and are able to achieve
competitive recognition performance.
