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Minimal Interaction Edge Tuning: A New Paradigm for Visual Adaptation

The rapid scaling of large vision pretrained models makes fine-tuning tasks more and more difficult on edge devices with low computational resources. We explore a new visual adaptation paradigm called edge tuning, which treats large pretrained models as standalone feature extractors that run on powerful cloud servers. The fine-tuning carries out on edge devices with small networks which require low computational resources. Existing methods that are potentially suitable for our edge tuning paradigm are discussed. But, three major drawbacks hinder their application in edge tuning: low adaptation capability, large adapter network, and high information transfer overhead. To address these issues, we propose Minimal Interaction Edge Tuning, or MIET, which reveals that the sum of intermediate features from pretrained models not only has minimal information transfer but also has high adaptation capability. With a lightweight attention-based adaptor network, MIET achieves information transfer efficiency, parameter efficiency, computational and memory efficiency, and at the same time demonstrates competitive results on various visual adaptation benchmarks.

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