Models for fine-grained image classification tasks, where the difference
between some classes can be extremely subtle and the number of samples per
class tends to be low, are particularly prone to picking up background-related
biases and demand robust methods to handle potential examples with
out-of-distribution (OOD) backgrounds. To gain deeper insights into this
critical problem, our research investigates the impact of background-induced
bias on fine-grained image classification, evaluating standard backbone models
such as Convolutional Neural Network (CNN) and Vision Transformers (ViT). We
explore two masking strategies to mitigate background-induced bias: Early
masking, which removes background information at the (input) image level, and
late masking, which selectively masks high-level spatial features corresponding
to the background. Extensive experiments assess the behavior of CNN and ViT
models under different masking strategies, with a focus on their generalization
to OOD backgrounds. The obtained findings demonstrate that both proposed
strategies enhance OOD performance compared to the baseline models, with early
masking consistently exhibiting the best OOD performance. Notably, a ViT
variant employing GAP-Pooled Patch token-based classification combined with
early masking achieves the highest OOD robustness.

我们的研究调查了背景引起的偏见对细粒度图像分类的影响，并评估了卷积神经网络和视觉变换器等标准骨干模型在不同遮罩策略下的行为，得出早期遮罩在 ODD 性能方面表现最佳的结论。