AIDE: An Automatic Data Engine for Object Detection in Autonomous Driving
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arXiv
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| Main Authors: | , , , , , , |
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866913283585343488 |
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| author | Liang, Mingfu Su, Jong-Chyi Schulter, Samuel Garg, Sparsh Zhao, Shiyu Wu, Ying Chandraker, Manmohan |
| author_facet | Liang, Mingfu Su, Jong-Chyi Schulter, Samuel Garg, Sparsh Zhao, Shiyu Wu, Ying Chandraker, Manmohan |
| contents | Autonomous vehicle (AV) systems rely on robust perception models as a cornerstone of safety assurance. However, objects encountered on the road exhibit a long-tailed distribution, with rare or unseen categories posing challenges to a deployed perception model. This necessitates an expensive process of continuously curating and annotating data with significant human effort. We propose to leverage recent advances in vision-language and large language models to design an Automatic Data Engine (AIDE) that automatically identifies issues, efficiently curates data, improves the model through auto-labeling, and verifies the model through generation of diverse scenarios. This process operates iteratively, allowing for continuous self-improvement of the model. We further establish a benchmark for open-world detection on AV datasets to comprehensively evaluate various learning paradigms, demonstrating our method's superior performance at a reduced cost. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2403_17373 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | AIDE: An Automatic Data Engine for Object Detection in Autonomous Driving Liang, Mingfu Su, Jong-Chyi Schulter, Samuel Garg, Sparsh Zhao, Shiyu Wu, Ying Chandraker, Manmohan Computer Vision and Pattern Recognition Artificial Intelligence Machine Learning Autonomous vehicle (AV) systems rely on robust perception models as a cornerstone of safety assurance. However, objects encountered on the road exhibit a long-tailed distribution, with rare or unseen categories posing challenges to a deployed perception model. This necessitates an expensive process of continuously curating and annotating data with significant human effort. We propose to leverage recent advances in vision-language and large language models to design an Automatic Data Engine (AIDE) that automatically identifies issues, efficiently curates data, improves the model through auto-labeling, and verifies the model through generation of diverse scenarios. This process operates iteratively, allowing for continuous self-improvement of the model. We further establish a benchmark for open-world detection on AV datasets to comprehensively evaluate various learning paradigms, demonstrating our method's superior performance at a reduced cost. |
| title | AIDE: An Automatic Data Engine for Object Detection in Autonomous Driving |
| topic | Computer Vision and Pattern Recognition Artificial Intelligence Machine Learning |
| url | https://arxiv.org/abs/2403.17373 |