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[pdf]
[supp]
[arXiv]
[bibtex]@InProceedings{Liang_2024_CVPR, author = {Liang, Mingfu and Su, Jong-Chyi and Schulter, Samuel and Garg, Sparsh and Zhao, Shiyu and Wu, Ying and Chandraker, Manmohan}, title = {AIDE: An Automatic Data Engine for Object Detection in Autonomous Driving}, booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)}, month = {June}, year = {2024}, pages = {14695-14706} }
AIDE: An Automatic Data Engine for Object Detection in Autonomous Driving
Abstract
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.
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