Summary of Activeanno3d — An Active Learning Framework For Multi-modal 3d Object Detection, by Ahmed Ghita et al.
ActiveAnno3D – An Active Learning Framework for Multi-Modal 3D Object Detection
by Ahmed Ghita, Bjørk Antoniussen, Walter Zimmer, Ross Greer, Christian Creß, Andreas Møgelmose, Mohan M. Trivedi, Alois C. Knoll
First submitted to arxiv on: 5 Feb 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Machine Learning (cs.LG)
GrooveSquid.com Paper Summaries
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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 addresses the issue of curating large-scale datasets, which remains a time-consuming and resource-intensive challenge. The authors propose ActiveAnno3D, an active learning framework that selects data samples for labeling based on maximum informativeness for training. They explore various continuous training methods and integrate the most efficient method regarding computational demand and detection performance. Experiments are conducted on the nuScenes and TUM Traffic Intersection datasets using BEVFusion and PV-RCNN models. The results show that ActiveAnno3D can achieve similar performance to full-labeling data with half the training data, reducing labeling costs. The framework is integrated into the proAnno labeling tool to enable AI-assisted data selection and labeling. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper solves a big problem in making datasets for computers to learn from. Right now, it takes a lot of time and money to make these datasets because they need to be labeled by humans. The authors came up with a way to use active learning to select the most important data samples to label. This can save a lot of time and money compared to labeling all the data. They tested their method on two big datasets and showed that it works just as well as labeling all the data, but takes much less time and effort. |
Keywords
* Artificial intelligence * Active learning * Rcnn
