ForAug: Recombining Foregrounds and Backgrounds to Improve Vision Transformer Training with Bias Mitigation

arXiv:2503.09399v3 Announce Type: replace-cross Abstract: Transformers, particularly Vision Transformers (ViTs), have achieved state-of-the-art performance in large-scale image classification. However, they often require large amounts of data and can exhibit biases, such as center or size bias, that limit their robustness and generalizability. This paper introduces ForAug, a novel data augmentation operation that addresses these challenges by explicitly imposing invariances into the training data, which are otherwise part of the neural network architecture. ForAug is constructed by using pretrained foundation models to separate and recombine foreground objects with different backgrounds. This recombination step enables us to take fine-grained control over object position and size, as well as background selection. We demonstrate that using ForAug significantly improves the accuracy of ViTs and other architectures by up to 4.5 percentage points (p.p.) on ImageNet, which translates to 7.3 p.p. on downstream tasks. Importantly, ForAug not only improves accuracy but also opens new ways to analyze model behavior and quantify biases. Namely, we introduce metrics for background robustness, foreground focus, center bias, and size bias and show that using ForAug during training substantially reduces these biases. In summary, ForAug provides a valuable tool for analyzing and mitigating biases, enabling the development of more robust and reliable computer vision models. Our code and dataset are publicly available at https://github.com/tobna/ForAug.