Quick ViTs: Speeding up Vision Transformers through Equivariance
📰 ArXiv cs.AI
Speed up Vision Transformers using equivariance to leverage geometric regularities in natural images
Action Steps
- Apply dihedral symmetry group to Vision Transformer architecture
- Implement reflection equivariance in ViT models
- Test the efficiency of Quick ViTs compared to traditional ViTs
- Configure Quick ViTs for various computer vision tasks
- Evaluate the performance of Quick ViTs on benchmark datasets
Who Needs to Know This
Computer vision engineers and researchers can benefit from this technique to improve the efficiency of their Vision Transformer models
Key Insight
💡 Equivariance can be used to improve the efficiency of Vision Transformers by leveraging geometric regularities in natural images
Share This
💡 Speed up Vision Transformers with equivariance!
Key Takeaways
Speed up Vision Transformers using equivariance to leverage geometric regularities in natural images
Full Article
Title: Quick ViTs: Speeding up Vision Transformers through Equivariance
Abstract:
arXiv:2505.15441v5 Announce Type: replace-cross Abstract: Natural images exhibit strong geometric regularities: local structures, such as edges, corners, and textures, appear in many orientations and mirror configurations. Since Vision Transformers (ViTs) operate on square image patches, these transformations naturally correspond to the dihedral symmetry group $\mathrm{D}_8$, also known as the octic group. Recent work has shown that ViTs can be made reflection equivariant and more efficient than
Abstract:
arXiv:2505.15441v5 Announce Type: replace-cross Abstract: Natural images exhibit strong geometric regularities: local structures, such as edges, corners, and textures, appear in many orientations and mirror configurations. Since Vision Transformers (ViTs) operate on square image patches, these transformations naturally correspond to the dihedral symmetry group $\mathrm{D}_8$, also known as the octic group. Recent work has shown that ViTs can be made reflection equivariant and more efficient than
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