Summary of Laecips: Large Vision Model Assisted Adaptive Edge-cloud Collaboration For Iot-based Perception System, by Shijing Hu et al.
LAECIPS: Large Vision Model Assisted Adaptive Edge-Cloud Collaboration for IoT-based Perception System
by Shijing Hu, Ruijun Deng, Xin Du, Zhihui Lu, Qiang Duan, Yi He, Shih-Chia Huang, Jie Wu
First submitted to arxiv on: 16 Apr 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Computer Vision and Pattern Recognition (cs.CV); Distributed, Parallel, and Cluster Computing (cs.DC)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed framework, LAECIPS, tackles the challenge of deploying large vision models in resource-constrained IoT environments. By combining edge-cloud collaboration with co-inference, LAECIPS achieves high inference accuracy and low latency, making it suitable for real-time applications like autonomous driving and robotics. The framework consists of a large vision model on the cloud and a lightweight model on the edge, which are designed to be plug-and-play. An optimized strategy based on hard input mining ensures high accuracy and low latency. Furthermore, LAECIPS updates its edge model and collaboration strategy under the supervision of the large vision model, allowing it to adapt to dynamic IoT data streams. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary LAECIPS is a new way for computers to work together to recognize things in pictures and videos. It’s special because it can do this quickly and accurately even when there are lots of different devices involved. This is important because some applications need real-time results, like self-driving cars or robots that need to understand what they see. The LAECIPS system uses two models: a big one on the cloud (the internet) and a small one on each device. It makes sure these models work together smoothly and can adapt to changes in the data it’s processing. |
Keywords
» Artificial intelligence » Inference
