Mobile-Former: Bridging MobileNet and Transformer

We present Mobile-Former, a parallel design of MobileNet and transformer with a two-way bridge in between. Additionally, we build an efficient end-to-end detector by replacing backbone, encoder and decoder in DETR with Mobile-Former, which outperforms DETR by 1.3 AP but saves 52% of computational cost and 36% of parameters.

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

• 1 This structure leverages the advantages of MobileNet at local processing and transformer at global interaction.
• 2 And the bridge enables bidirectional fusion of local and global features.
• 3 Different from recent works on vision transformer, the transformer in Mobile-Former contains very few tokens (e.g.

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

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