CMT: Convolutional Neural Networks Meet Vision Transformers

Vision transformers have been successfully applied to image recognition tasks due to their ability to capture long-range dependencies within an image. In particular, our CMT-S achieves 83.5% top-1 accuracy on ImageNet, while being 14x and 2x smaller on FLOPs than the existing DeiT and EfficientNet, respectively.

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• 1 However, there are still gaps in both performance and computational cost between transformers and existing convolutional neural networks (CNNs).
• 2 In this paper, we aim to address this issue and develop a network that can outperform not only the canonical transformers, but also the high-performance convolutional models.
• 3 We propose a new transformer based hybrid network by taking advantage of transformers to capture long-range dependencies, and of CNNs to extract local information.

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

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