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\section{Discussion \& Conclusion}
\label{sec:conclusion}

We introduce \schemename, a novel data augmentation scheme that facilitates improved Transformer training for image classification.
By explicitly separating and recombining foreground objects and backgrounds, \schemename enables controlled data augmentation beyond existing image compositions, leading to significant performance gains on ImageNet and downstream fine-grained classification tasks.
Furthermore, \schemename provides a powerful framework for analyzing model behavior and quantifying biases, including background robustness, foreground focus, center bias, and size bias.
Our experiments demonstrate that training on \name, the instantiation of \schemename on ImageNet, not only boosts accuracy but also significantly reduces these biases, resulting in more robust and generalizable models.
In the future, we see \schemename be also applied to other datasets and tasks, like video recognition or segmentation.
\schemename's ability to both improve performance and provide insights into model behavior makes it a valuable tool for advancing CV research and developing more reliable AI systems.