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AdvancingFine-GrainedClassificationbyStructure andSubjectPreservingAugmentation

Neural Information Processing Systems

Fine-grained visual classification (FGVC) involves classifying closely related sub-classes. This task is difficult due to the subtle differences between classes and the high intra-class variance. Moreover, FGVC datasets are typically small and challenging to gather, thus highlighting a significant need for effective data augmentation.