CrossViT: Cross-Attention Multi-Scale Vision Transformer for Image Classification

The recently developed vision transformer (ViT) has achieved promising results on image classification compared to convolutional neural networks. For example, on the ImageNet1K dataset, with some architectural changes, our approach outperforms the recent DeiT by a large margin of 2% with a small to moderate increase in FLOPs and model parameters.

⚡ This is an original paraphrased summary — not copied from the abstract. Full paper available at the source link below.

• 1 Inspired by this, in this paper, we study how to learn multi-scale feature representations in transformer models for image classification.
• 2 To this end, we propose a dual-branch transformer to com-bine image patches (i.e., tokens in a transformer) of different sizes to produce stronger image features.
• 3 Our approach processes small-patch and large-patch tokens with two separate branches of different computational complexity and these tokens are then fused purely by attention multiple times to complement each other.

This research advances how AI systems learn, reason, and solve problems — with direct implications for automation and scientific discovery.

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