AIDE: An Automatic Data Engine for Object Detection in Autonomous Driving
<|reference_start|>AIDE: An Automatic Data Engine for Object Detection in Autonomous Driving: 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.<|reference_end|>
@article{liang2024aide:,
    title={AIDE: An Automatic Data Engine for Object Detection in Autonomous
  Driving},
    author={Mingfu Liang, Jong-Chyi Su, Samuel Schulter, Sparsh Garg, Shiyu Zhao,
  Ying Wu, Manmohan Chandraker},
    journal={arXiv preprint arXiv:2403.17373},
    year={2024},
    archivePrefix={arXiv},
    eprint={2403.17373},
    primaryClass={cs.CV cs.AI cs.LG}
}