  The task of fine-grained visual classification (FGVC) deals with
classification problems that display a small inter-class variance such as
distinguishing between different bird species or car models. State-of-the-art
approaches typically tackle this problem by integrating an elaborate attention
mechanism or (part-) localization method into a standard convolutional neural
network (CNN). Also in this work the aim is to enhance the performance of a
backbone CNN such as ResNet by including three efficient and lightweight
components specifically designed for FGVC. This is achieved by using global
k-max pooling, a discriminative embedding layer trained by optimizing class
means and an efficient bounding box estimator that only needs class labels for
training. The resulting model achieves new best state-of-the-art recognition
accuracies on the Stanford cars and FGVC-Aircraft datasets.
