The Role of Shape for Domain Generalization on Sparsely-Textured Images
The Role of Shape for Domain Generalization on Sparsely-Textured Images
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DOI：
10.1109/cvprw56347.2022.00560
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发表时间：
2022-06
期刊：
影响因子：
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通讯作者：
N. Nazari;Adriana Kovashka
中科院分区：
文献类型：
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作者：
N. Nazari;Adriana Kovashka
最先进的物体识别方法不能很好地推广到看不见的领域。领域泛化方面的工作试图通过增加功能兼容性来桥接领域，但重点关注标准的、基于外观的表示。我们展示了基于形状的表示在提高领域鲁棒性方面的潜力。我们比较两种基于形状的表示：一种在边缘特征上训练卷积网络，另一种计算软的、密集的中轴变换。我们展示了这些表示对于不同类型域的互补优势，以及保留的纹理量的影响。我们表明，基于形状的技术可以更好地利用数据增强来进行领域泛化，并且在减轻纹理偏差方面比形状诱导增强更有效。最后，我们表明，当最先进的领域泛化方法中的卷积网络被替换为显式捕获形状的网络时，我们获得了改进的结果。
State-of-the-art object recognition methods do not generalize well to unseen domains. Work in domain generalization has attempted to bridge domains by increasing feature compatibility, but has focused on standard, appearance-based representations. We show the potential of shape-based representations to increase domain robustness. We compare two types of shape-based representations: one trains a convolutional network over edge features, and another computes a soft, dense medial axis transform. We show the complementary strengths of these representations for different types of domains, and the effect of the amount of texture that is preserved. We show that our shape-based techniques better leverage data augmentations for domain generalization, and are more effective at texture bias mitigation than shape-inducing augmentations. Finally, we show that when the convolutional network in state-of-the-art domain generalization methods is replaced with one that explicitly captures shape, we obtain improved results.