  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. Recent advancements in text-to-image diffusion
models offer new possibilities for augmenting classification datasets. While
these models have been used to generate training data for classification tasks,
their effectiveness in full-dataset training of FGVC models remains
under-explored. Recent techniques that rely on Text2Image generation or Img2Img
methods, often struggle to generate images that accurately represent the class
while modifying them to a degree that significantly increases the dataset's
diversity. To address these challenges, we present SaSPA: Structure and Subject
Preserving Augmentation. Contrary to recent methods, our method does not use
real images as guidance, thereby increasing generation flexibility and
promoting greater diversity. To ensure accurate class representation, we employ
conditioning mechanisms, specifically by conditioning on image edges and
subject representation. We conduct extensive experiments and benchmark SaSPA
against both traditional and recent generative data augmentation methods. SaSPA
consistently outperforms all established baselines across multiple settings,
including full dataset training, contextual bias, and few-shot classification.
Additionally, our results reveal interesting patterns in using synthetic data
for FGVC models; for instance, we find a relationship between the amount of
real data used and the optimal proportion of synthetic data. Code is available
at https://github.com/EyalMichaeli/SaSPA-Aug.
