Summary of Dual-model Distillation For Efficient Action Classification with Hybrid Edge-cloud Solution, by Timothy Wei et al.
Dual-Model Distillation for Efficient Action Classification with Hybrid Edge-Cloud Solution
by Timothy Wei, Hsien Xin Peng, Elaine Xu, Bryan Zhao, Lei Ding, Diji Yang
First submitted to arxiv on: 16 Oct 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper addresses the challenge of deploying Large Video-Language (VLM) models in real-world applications due to hardware limitations and computational costs. The authors propose a hybrid edge-cloud solution that combines the efficiency of smaller models for local processing with the accuracy of larger cloud-based models when necessary. A novel unsupervised data generation method, Dual-Model Distillation (DMD), is designed to train a lightweight switcher model that can predict when the edge model’s output is uncertain and selectively offload inference to the large model in the cloud. Experimental results on action classification tasks show that this framework not only reduces computational overhead but also improves accuracy compared to using a single large model. The solution has potential applications beyond healthcare, providing a scalable and adaptable approach for resource-constrained environments. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps solve a big problem with using really powerful AI models in real-life situations because they use too much computer power and are slow. The authors came up with a way to make it work better by combining the strengths of smaller and larger models together. They made a new way to generate data that helps decide when to use each model, making it more efficient and accurate. This might help people do things like diagnose medical conditions faster or improve AI-powered robots for tasks. It’s an important step forward in using powerful AI models in everyday life. |
Keywords
» Artificial intelligence » Classification » Distillation » Inference » Unsupervised
