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REViT-v2: Hierarchical Windowed Roto-reflection Equivariant ViT for Equivariant Feature Extraction

2026-10-06 01:23:15
Sheir A. Zaheer, Jihwan Moon, Chan Y. Park
arXiv_CV
           
Abstract

We propose a scalable roto-reflection-group-equivariant vision transformer based on windowed group-convolutional self-attention and a hierarchical feature architecture. We demonstrate that our approach can be scaled to group-equivariant vision transformers (ViTs) with millions of parameters and large datasets with practically sized images, i.e., ImageNet. The code and pretrained weights for the proposed Hierarchical Windowed Roto-reflection Equivariant ViTs (REViT-v2) are available at this https URL.

Abstract (translated)

URL

https://arxiv.org/abs/2610.07585

PDF

https://arxiv.org/pdf/2610.07585.pdf

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