Novel Object Detection refers to the task of identifying and localizing objects in images or videos that belong to previously unseen classes or categories. Traditional object detection systems are typically trained on a fixed set of object categories and struggle to recognize objects that are not included in their training data. In contrast, novel object detection systems aim to generalize to new, unseen object classes by leveraging techniques such as few-shot learning, zero-shot learning, or transfer learning.

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AIDE: An Automatic Data Engine for Object Detection in Autonomous Driving

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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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