Summary of Language-driven Active Learning For Diverse Open-set 3d Object Detection, by Ross Greer et al.
Language-Driven Active Learning for Diverse Open-Set 3D Object Detection
by Ross Greer, Bjørk Antoniussen, Andreas Møgelmose, Mohan Trivedi
First submitted to arxiv on: 19 Apr 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper proposes VisLED, a language-driven active learning framework for diverse open-set 3D object detection in autonomous driving scenarios. The 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. The Vision-Language Embedding Diversity Querying (VisLED-Querying) algorithm operates in both open-world exploring and closed-world mining settings, selecting data points most novel relative to existing data in open-world exploring and mining novel instances of known classes in closed-world mining. The approach is evaluated on the nuScenes dataset, demonstrating its efficiency compared to random sampling and entropy-querying methods. The results show that VisLED-Querying consistently outperforms random sampling and offers competitive performance compared to entropy-querying. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary In this paper, researchers developed a new way to help self-driving cars detect objects more accurately. They created a system called VisLED that uses language to learn about new objects and situations. This helps the car’s computer to better recognize things it hasn’t seen before. The team tested their approach on a dataset of 3D scenes and found that it worked well, even beating other methods at detecting certain types of objects. |
Keywords
» Artificial intelligence » Active learning » Embedding » Object detection
