Language-Driven Active Learning for Diverse Open-Set 3D Object Detection

Ross Greer, Bjørk Antoniussen, Andreas Møgelmose, Mohan Trivedi; Proceedings of the Winter Conference on Applications of Computer Vision (WACV) Workshops, 2025, pp. 980-988

Object detection is crucial for ensuring safe autonomous driving. However data-driven approaches face challenges when encountering minority or novel objects in the 3D driving scene. In this paper we propose VisLED a language-driven active learning framework for diverse open-set 3D Object Detection. Our method leverages active learning techniques to query diverse and informative data samples from an unlabeled pool enhancing the model's ability to detect underrepresented or novel objects. Specifically we introduce the Vision-Language Embedding Diversity Querying (VisLED-Querying) algorithm which operates in both open-world exploring and closed-world mining settings. In open-world exploring VisLED-Querying selects data points most novel relative to existing data while in closed-world mining it mines novel instances of known classes. We evaluate our approach on the nuScenes dataset and demonstrate its efficiency compared to random sampling and entropy-querying methods for training 3D object detection models. Our results show that VisLED-Querying consistently outperforms random sampling and offers competitive performance compared to entropy-querying despite the latter's model-optimality highlighting the potential of VisLED for improving object detection in autonomous driving scenarios. We make our code publicly available at https://github.com/Bjork-crypto/VisLED-Querying