A Unified Framework for Vision Transformers Equivariant to Discrete Subgroups of $\mathrm{O}(2)$
arXiv:2606.27864v1 Announce Type: cross Abstract: Vision transformers have become a dominant architecture for visual recognition. However, standard models do not explicitly encode the planar symmetries that arise in many vision domains. We introduce a family of vision transformers equivariant to arbitrary discrete subgroups of $\mathrm{O}(2)$, providing a unified framework that generalizes prior flipping- and $D_4$-equivariant transformer architectures. Our construction yields equivariant analogues of the core transformer components, together with expressivity guarantees for the resulting laye
This research is emerging now as the limitations of standard Vision Transformers in handling planar symmetries in visual data become a bottleneck in advanced computer vision applications.
A unified framework for equivariant Vision Transformers promises more robust, efficient, and generalizable AI models for visual recognition across diverse applications, reducing data requirements and improving performance.
Vision Transformers can now systematically incorporate important geometric prior knowledge, leading to improved performance in tasks requiring rotational and reflective invariance without extensive data augmentation.
- · AI/ML researchers
- · Computer Vision developers
- · Robotics
- · Medical imaging
- · Developers relying solely on brute-force data augmentation for symmetry
Improved accuracy and data efficiency in vision AI across various domains.
Accelerated development of AI systems that operate effectively in complex, real-world physical environments.
Enhanced capabilities for autonomous systems to interpret and interact with their surroundings more intelligently.
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Read at arXiv cs.LG