Language-Driven Active Learning for Diverse Open-Set 3D Object Detection
This addresses the challenge of underrepresented or novel object detection for autonomous driving, presenting an incremental improvement over existing active learning techniques.
The paper tackles the problem of detecting minority or novel objects in 3D driving scenes by proposing VisLED, a language-driven active learning framework that queries diverse data samples to enhance detection. Results on the nuScenes dataset show it consistently outperforms random sampling and offers competitive performance compared to entropy-querying methods.
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. 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