Related papers: The Why, When, and How to Use Active Learning in Large-Data-Driven 3D Object Detection for Safe Autonomous Driving: An Empirical Exploration

The Why, When, and How to Use Active Learning in Large-Data-Driven 3D Object Detection for Safe Autonomous Driving: An Empirical Exploration

URL: http://arxiv.org/abs/2401.16634v1
Date: Tue, 30 Jan 2024 00:14:13 GMT
Title: The Why, When, and How to Use Active Learning in Large-Data-Driven 3D Object Detection for Safe Autonomous Driving: An Empirical Exploration
Authors: Ross Greer, Bj{\o}rk Antoniussen, Mathias V. Andersen, Andreas M{\o}gelmose, and Mohan M. Trivedi
Abstract summary: entropy querying is a promising strategy for selecting data that enhances model learning in resource-constrained environments. Our findings suggest that entropy querying is a promising strategy for selecting data that enhances model learning in resource-constrained environments.
Score: 1.2815904071470705
License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Abstract: Active learning strategies for 3D object detection in autonomous driving datasets may help to address challenges of data imbalance, redundancy, and high-dimensional data. We demonstrate the effectiveness of entropy querying to select informative samples, aiming to reduce annotation costs and improve model performance. We experiment using the BEVFusion model for 3D object detection on the nuScenes dataset, comparing active learning to random sampling and demonstrating that entropy querying outperforms in most cases. The method is particularly effective in reducing the performance gap between majority and minority classes. Class-specific analysis reveals efficient allocation of annotated resources for limited data budgets, emphasizing the importance of selecting diverse and informative data for model training. Our findings suggest that entropy querying is a promising strategy for selecting data that enhances model learning in resource-constrained environments.

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