可调软等变性及其保证

摘要

等变性是计算机视觉模型的基本属性，然而现实数据中严格等变条件鲜有满足，这会限制模型性能。因此控制等变程度显得尤为重要。我们提出了一种通用框架，通过将模型权重投影至设计子空间来构建软等变模型。该方法适用于任何预训练架构，并能从理论上约束诱导等变误差。实验方面，我们在包括ViT和ResNet在内的多种预训练骨干网络上验证了方法的有效性，覆盖图像分类、语义分割和人类轨迹预测等任务。值得注意的是，在竞争激烈的ImageNet基准测试中，我们的方法在降低等变误差的同时显著提升了模型性能。

English

Equivariance is a fundamental property in computer vision models, yet strict equivariance is rarely satisfied in real-world data, which can limit a model's performance. Controlling the degree of equivariance is therefore desirable. We propose a general framework for constructing soft equivariant models by projecting the model weights into a designed subspace. The method applies to any pre-trained architecture and provides theoretical bounds on the induced equivariance error. Empirically, we demonstrate the effectiveness of our method on multiple pre-trained backbones, including ViT and ResNet, across image classification, semantic segmentation, and human-trajectory prediction tasks. Notably, our approach improves the performance while simultaneously reducing equivariance error on the competitive ImageNet benchmark.

可调软等变性及其保证

Tunable Soft Equivariance with Guarantees

摘要

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