Abstract: Since the invention of Transformers, attention-based models have been widely used in ... functions can reduce the number of active activations and enable sparse matrix multiplications in ...

recursive layer-wise matrix-matrix multiplication to aggregate attention and select features from last layer, contrastive loss Feature Fusion Vision Transformer for Fine-Grained Visual Categorization.

Peptides designed by artificial intelligence restrict both drug-resistant bacteria and rapidly evolving viruses.

We leverage a triple attention-aided vision transformer (TrpViT) architecture, which uses a vision-centric approach within the transformer network to enhance global information acquisition. The TrpViT ...

but does not suffer the drop in performance or limitation to only one input modality seen with other efficient Transformer-based approaches. BiXT is inspired by the Perceiver architectures but ...

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