Steerable Transformers for Volumetric Data
Fuente:
arXiv
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| Autori principali: | , |
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| _version_ | 1866911230174691328 |
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| author | Kundu, Soumyabrata Kondor, Risi |
| author_facet | Kundu, Soumyabrata Kondor, Risi |
| contents | We introduce Steerable Transformers, an extension of the Vision Transformer mechanism that maintains equivariance to the special Euclidean group $\mathrm{SE}(d)$. We propose an equivariant attention mechanism that operates on features extracted by steerable convolutions. Operating in Fourier space, our network utilizes Fourier space non-linearities. Our experiments in both two and three dimensions show that adding steerable transformer layers to steerable convolutional networks enhances performance. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2405_15932 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Steerable Transformers for Volumetric Data Kundu, Soumyabrata Kondor, Risi Computer Vision and Pattern Recognition We introduce Steerable Transformers, an extension of the Vision Transformer mechanism that maintains equivariance to the special Euclidean group $\mathrm{SE}(d)$. We propose an equivariant attention mechanism that operates on features extracted by steerable convolutions. Operating in Fourier space, our network utilizes Fourier space non-linearities. Our experiments in both two and three dimensions show that adding steerable transformer layers to steerable convolutional networks enhances performance. |
| title | Steerable Transformers for Volumetric Data |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2405.15932 |