Summary of Aide: An Automatic Data Engine For Object Detection in Autonomous Driving, by Mingfu Liang et al.
AIDE: An Automatic Data Engine for Object Detection in Autonomous Driving
by Mingfu Liang, Jong-Chyi Su, Samuel Schulter, Sparsh Garg, Shiyu Zhao, Ying Wu, Manmohan Chandraker
First submitted to arxiv on: 26 Mar 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
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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 The proposed Automatic Data Engine (AIDE) aims to improve autonomous vehicle (AV) perception models by leveraging vision-language and large language models. AIDE automatically identifies issues, curates data efficiently, auto-labels the model, and verifies it through diverse scenario generation. This iterative process enables continuous self-improvement of the model. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Imagine you’re trying to teach a car’s computer system to recognize objects on the road. This is called perception modeling, and it’s super important for keeping us safe while driving. Right now, making these models is really hard because they have trouble recognizing rare or weird things that might be on the road. To fix this, scientists came up with an idea to use special language models to help train the computer system. They call this the Automatic Data Engine (AIDE). It helps by finding problems, fixing data issues, and even testing the model itself. This makes the whole process faster and better. |
