mlsanityData to 7 Oct 2026

Scalable Active Learning for Object Detection

arXiv 2004.04699 ↗First posted 9 Apr 2020cs.CVcited by 58

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Abstract (arXiv)

Deep Neural Networks trained in a fully supervised fashion are the dominant technology in perception-based autonomous driving systems. While collecting large amounts of unlabeled data is already a major undertaking, only a subset of it can be labeled by humans due to the effort needed for high-quality annotation. Therefore, finding the right data to label has become a key challenge. Active learning is a powerful technique to improve data efficiency for supervised learning methods, as it aims at selecting the smallest possible training set to reach a required performance. We have built a scalable production system for active learning in the domain of autonomous driving. In this paper, we describe the resulting high-level design, sketch some of the challenges and their solutions, present our current results at scale, and briefly describe the open problems and future directions.

Key facts. Cited by 58 papers in the map (data through 7 Oct 2026). First posted on arXiv 9 Apr 2020.

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 11; 2022: 17; 2023: 15; 2024: 7; 2025: 5; 2026: 3.

58 papers.

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  1. 2022TransFuser: Imitation with Transformer-Based Sensor Fusion for Autonomous Drivingcited by 477
  2. 2022A Comparative Survey of Deep Active Learningcited by 88
  3. 2021Influence Selection for Active Learningcited by 82
  4. 2021Active Learning for Deep Object Detection via Probabilistic Modelingcited by 79
  5. 2021Learning 3D Semantic Segmentation with only 2D Image Supervisioncited by 60
  6. 2023Radar-Camera Fusion for Object Detection and Semantic Segmentation in Autonomous Driving: A Comprehensive Reviewcited by 57
  7. 2021Consistency-based Active Learning for Object Detectioncited by 32
  8. 2023Active learning for data streams: a surveycited by 29
  9. 2021Not All Labels Are Equal: Rationalizing The Labeling Costs for Training Object Detectioncited by 27
  10. 2021TALISMAN: Targeted Active Learning for Object Detection with Rare Classes and Slices using Submodular Mutual Informationcited by 27
  11. 2021Taxonomy of Machine Learning Safety: A Survey and Primercited by 22
  12. 2022Bamboo: Building Mega-Scale Vision Dataset Continually with Human-Machine Synergycited by 20
38 more
  1. 2022Active Learning Strategies for Weakly-supervised Object Detectioncited by 17
  2. 2024The Why, When, and How to Use Active Learning in Large-Data-Driven 3D Object Detection for Safe Autonomous Driving: An Empirical Explorationcited by 17
  3. 2022Semantic Segmentation with Active Semi-Supervised Learningcited by 16
  4. 2022Active Pointly-Supervised Instance Segmentationcited by 15
  5. 2024Language-Driven Active Learning for Diverse Open-Set 3D Object Detectioncited by 15
  6. 2024Visual Prompt Selection for In-Context Learning Segmentationcited by 12
  7. 2023Gaussian Switch Sampling: A Second Order Approach to Active Learningcited by 10
  8. 2023Monocular 3D Object Detection with LiDAR Guided Semi Supervised Active Learningcited by 10
  9. 2023Towards Free Data Selection with General-Purpose Modelscited by 10
  10. 2022THMA: Tencent HD Map AI System for Creating HD Map Annotationscited by 9
  11. 2023Navya3DSeg -- Navya 3D Semantic Segmentation Dataset & split generation for autonomous vehiclescited by 9
  12. 2023FOCAL: A Cost-Aware Video Dataset for Active Learningcited by 9
  13. 2024SSE: Multimodal Semantic Data Selection and Enrichment for Industrial-scale Data Assimilationcited by 7
  14. 2021Diverse Complexity Measures for Dataset Curation in Self-drivingcited by 6
  15. 2021Towards General and Efficient Active Learningcited by 6
  16. 2022ALBench: A Framework for Evaluating Active Learning in Object Detectioncited by 6
  17. 2025Technical Report for Argoverse2 Scenario Mining Challenges on Iterative Error Correction and Spatially-Aware Promptingcited by 6
  18. 2022Boosting Robustness Verification of Semantic Feature Neighborhoodscited by 4
  19. 2023ReBound: An Open-Source 3D Bounding Box Annotation Tool for Active Learningcited by 4
  20. 2022Design of Supervision-Scalable Learning Systems: Methodology and Performance Benchmarkingcited by 3
  21. 2022Pareto Optimization for Active Learning under Out-of-Distribution Data Scenarioscited by 3
  22. 2022Semantic Segmentation with Active Semi-Supervised Representation Learningcited by 3
  23. 2022Towards Rapid Prototyping and Comparability in Active Learning for Deep Object Detectioncited by 3
  24. 2023Active Learning for Object Detection with Non-Redundant Informative Samplingcited by 3
  25. 2021Just Label What You Need: Fine-Grained Active Selection for Perception and Prediction through Partially Labeled Scenescited by 2
  26. 2022Query-based Hard-Image Retrieval for Object Detection at Test Timecited by 2
  27. 2023Deep Active Learning with Noisy Oracle in Object Detectioncited by 2
  28. 2025Streamlining the Development of Active Learning Methods in Real-World Object Detectioncited by 2
  29. 2022Can I see an Example? Active Learning the Long Tail of Attributes and Relationscited by 1
  30. 2022Towards an Error-free Deep Occupancy Detector for Smart Camera Parking Systemcited by 1
  31. 2023Exploiting Counter-Examples for Active Learning with Partial labelscited by 1
  32. 2023Classification Committee for Active Deep Object Detectioncited by 1
  33. 2024Deep Active Learning: A Reality Checkcited by 1
  34. 2024STONE: A Submodular Optimization Framework for Active 3D Object Detectioncited by 1
  35. 2024Targeting Negative Flips in Active Learning using Validation Setscited by 1
  36. 2025Inconsistency-based Active Learning for LiDAR Object Detectioncited by 1
  37. 2025Table Detection with Active Learningcited by 1
  38. 2021Knowledge-driven Active Learningcited by 0

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