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The task of fine-grained visual classification (FGVC) deals withclassification problems that display a small inter-class variance such asdistinguishing between different bird species or car models. State-of-the-artapproaches typically tackle this problem by integrating an elaborate attentionmechanism or (part-) localization method into a standard convolutional neuralnetwork (CNN). Also in this work the aim is to enhance the performance of abackbone CNN such as ResNet by including three efficient and lightweightcomponents specifically designed for FGVC. This is achieved by using globalk-max pooling, a discriminative embedding layer trained by optimizing classmeans and an efficient bounding box estimator that only needs class labels fortraining. The resulting model achieves new best state-of-the-art recognitionaccuracies on the Stanford cars and FGVC-Aircraft datasets.

ELoPE: Fine-Grained Visual Classification with Efficient Localization, Pooling and Embedding