Group Equivariant Stand-Alone Self-Attention For Vision
October 02, 2020 Β· Declared Dead Β· π International Conference on Learning Representations
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Authors
David W. Romero, Jean-Baptiste Cordonnier
arXiv ID
2010.00977
Category
cs.CV: Computer Vision
Cross-listed
stat.ML
Citations
70
Venue
International Conference on Learning Representations
Last Checked
4 months ago
Abstract
We provide a general self-attention formulation to impose group equivariance to arbitrary symmetry groups. This is achieved by defining positional encodings that are invariant to the action of the group considered. Since the group acts on the positional encoding directly, group equivariant self-attention networks (GSA-Nets) are steerable by nature. Our experiments on vision benchmarks demonstrate consistent improvements of GSA-Nets over non-equivariant self-attention networks.
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