ShapeBlend: Boosting out-of-distribution robustness in image classification via shape-based blending augmentation

Deep Neural Networks (DNNs) often struggle to generalize beyond their training distributions, making them vulnerable to domain shifts. To enhance robustness, various approaches have been developed, particularly focusing on data-centric methods. Modifying the training data can increase diversity and improve generalization, while also introducing bias that positively guides the model’s decision-making. Prior research suggests that DNNs tend to overemphasize texture-based patterns, at the expense of more robust shape-based representations. We introduce ShapeBlend, a novel data augmentation technique that emphasizes image contours, and hence shape features. It blends a contour map from the push-pull CORF operator with the original image at varying strengths. ShapeBlend consistently outperforms state-of-the-art methods across major Out-of-Distribution (OOD) benchmarks (ImageNet-A, ImageNet-R, ImageNet-C, and ImageNet-C¯), setting new records in robustness. Moreover, ShapeBlend’s versatility allows its application during inference. To fully leverage ShapeBlend, we propose Shape-Enhanced Voting (SEV), an inference strategy that aggregates predictions from multiple ShapeBlend-processed images. The combination of ShapeBlend and SEV further enhances domain robustness, with performance gains varying based on the chosen configuration.