LESSViT introduces a low-rank efficient spatial-spectral attention mechanism and a hyperspectral masked autoencoder to improve generalization across spectral configuration shifts in hyperspectral imagery.
Tensor product attention is all you need
2 Pith papers cite this work. Polarity classification is still indexing.
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Cubit replaces Transformer's attention with a closed-form Kernel Ridge Regression token mixer and reports larger gains as training sequence length increases.
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LESSViT: Robust Hyperspectral Representation Learning under Spectral Configuration Shift
LESSViT introduces a low-rank efficient spatial-spectral attention mechanism and a hyperspectral masked autoencoder to improve generalization across spectral configuration shifts in hyperspectral imagery.
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Cubit: Token Mixer with Kernel Ridge Regression
Cubit replaces Transformer's attention with a closed-form Kernel Ridge Regression token mixer and reports larger gains as training sequence length increases.