Abstract
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.
Abstract (translated)
等变性是计算机视觉模型中的一个基本属性，然而在真实数据中严格等变性很少被满足，这可能限制模型的性能。因此，控制等变性的程度是可取的。我们提出了一种通过将模型权重投影到设计好的子空间来构建软等变模型的通用框架。该方法适用于任何预训练架构，并能对诱导的等变性误差提供理论上界。实验证明，我们的方法在多个预训练骨干网络（包括ViT和ResNet）上，在图像分类、语义分割和人体轨迹预测任务中均有效。值得注意的是，在竞争激烈的ImageNet基准测试中，我们的方法在提升性能的同时降低了等变性误差。
URL
https://arxiv.org/abs/2603.26657
