mlsanityData to 7 Oct 2026

Not All Labels Are Equal: Rationalizing The Labeling Costs for Training Object Detection

arXiv 2106.11921 ↗First posted 22 Jun 2021cs.CV cs.LGcited by 27

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Abstract (arXiv)

Deep neural networks have reached high accuracy on object detection but their success hinges on large amounts of labeled data. To reduce the labels dependency, various active learning strategies have been proposed, typically based on the confidence of the detector. However, these methods are biased towards high-performing classes and can lead to acquired datasets that are not good representatives of the testing set data. In this work, we propose a unified framework for active learning, that considers both the uncertainty and the robustness of the detector, ensuring that the network performs well in all classes. Furthermore, our method leverages auto-labeling to suppress a potential distribution drift while boosting the performance of the model. Experiments on PASCAL VOC07+12 and MS-COCO show that our method consistently outperforms a wide range of active learning methods, yielding up to a 7.7% improvement in mAP, or up to 82% reduction in labeling cost. Code will be released upon acceptance of the paper.

Key facts. Cited by 27 papers in the map (data through 7 Oct 2026). Builds on 26 of its 42 references that are in the map. First posted on arXiv 22 Jun 2021.

Builds on

26 of 42 references.

References · 26

  1. 2014Microsoft COCO: Common Objects in Contextcited by 17,498
  2. 2014Very Deep Convolutional Networks for Large-Scale Image Recognitioncited by 15,267
  3. 2015Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networkscited by 9,617
  4. 2015Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learningcited by 4,416
  5. 2015SSD: Single Shot MultiBox Detectorcited by 3,125
  6. 2020FixMatch: Simplifying Semi-Supervised Learning with Consistency and Confidencecited by 2,080
  7. 2019MixMatch: A Holistic Approach to Semi-Supervised Learningcited by 1,390
  8. 2016Temporal Ensembling for Semi-Supervised Learningcited by 1,054
  9. 2017Virtual Adversarial Training: A Regularization Method for Supervised and Semi-Supervised Learningcited by 918
  10. 2017Deep Bayesian Active Learning with Image Datacited by 812
  11. 2017Mean teachers are better role models: Weight-averaged consistency targets improve semi-supervised deep learning resultscited by 470
  12. 2019BatchBALD: Efficient and Diverse Batch Acquisition for Deep Bayesian Active Learningcited by 428
14 more
  1. 2019Learning Loss for Active Learningcited by 335
  2. 2020Contextual Diversity for Active Learningcited by 139
  3. 2021Humble Teachers Teach Better Students for Semi-Supervised Object Detectioncited by 115
  4. 2019Consistency-based Semi-supervised Active Learning: Towards Minimizing Labeling Costcited by 114
  5. 2021AdaMatch: A Unified Approach to Semi-Supervised Learning and Domain Adaptationcited by 104
  6. 2021Active Learning for Deep Object Detection via Probabilistic Modelingcited by 79
  7. 2021Multiple instance active learning for object detectioncited by 77
  8. 2019Active Learning for Deep Detection Neural Networkscited by 62
  9. 2020Scalable Active Learning for Object Detectioncited by 58
  10. 2019Bayesian Generative Active Deep Learningcited by 57
  11. 2018Localization-Aware Active Learning for Object Detectioncited by 48
  12. 2020Interpolation-based semi-supervised learning for object detectioncited by 40
  13. 2019Rethinking deep active learning: Using unlabeled data at model trainingcited by 39
  14. 2019An Adaptive Supervision Framework for Active Learning in Object Detectioncited by 20

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2022: 7; 2023: 9; 2024: 6; 2025: 4; 2026: 1.

27 papers.

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  1. 2022Plug and Play Active Learning for Object Detectioncited by 28
  2. 2023Exploring Active 3D Object Detection from a Generalization Perspectivecited by 26
  3. 2024AIDE: An Automatic Data Engine for Object Detection in Autonomous Drivingcited by 26
  4. 2022How Much More Data Do I Need? Estimating Requirements for Downstream Taskscited by 22
  5. 2022Improving the Intra-class Long-tail in 3D Detection via Rare Example Miningcited by 19
  6. 2022Active Learning Strategies for Weakly-supervised Object Detectioncited by 17
  7. 2023Identifying Label Errors in Object Detection Datasets by Loss Inspectioncited by 16
  8. 2022Optimizing Data Collection for Machine Learningcited by 15
  9. 2023Box-Level Active Detectioncited by 13
  10. 2023Monocular 3D Object Detection with LiDAR Guided Semi Supervised Active Learningcited by 10
  11. 2023Semi-supervised Active Learning for Video Action Detectioncited by 10
  12. 2024Computer Vision for Primate Behavior Analysis in the Wildcited by 10
15 more
  1. 2023Multi-Task Consistency for Active Learningcited by 9
  2. 2023ALWOD: Active Learning for Weakly-Supervised Object Detectioncited by 8
  3. 2024SPAMming Labels: Efficient Annotations for the Trackers of Tomorrowcited by 7
  4. 2024Annotations on a Budget: Leveraging Geo-Data Similarity to Balance Model Performance and Annotation Costcited by 5
  5. 2024Uncertainty Estimation for 3D Object Detection via Evidential Learningcited by 5
  6. 2022Towards Rapid Prototyping and Comparability in Active Learning for Deep Object Detectioncited by 3
  7. 2023Active Learning for Object Detection with Non-Redundant Informative Samplingcited by 3
  8. 2023Active Semi-Supervised Learning by Exploring Per-Sample Uncertainty and Consistencycited by 2
  9. 2022PercentMatch: Percentile-based Dynamic Thresholding for Multi-Label Semi-Supervised Classificationcited by 1
  10. 2024Deep Active Learning: A Reality Checkcited by 1
  11. 2025Inconsistency-based Active Learning for LiDAR Object Detectioncited by 1
  12. 2025Breaking the SSL-AL Barrier: A Synergistic Semi-Supervised Active Learning Framework for 3D Object Detectioncited by 0
  13. 2025HeAL3D: Heuristical-enhanced Active Learning for 3D Object Detectioncited by 0
  14. 2025SAM2Auto: Auto Annotation Using FLASHcited by 0
  15. 2026From Bounding Boxes to Visual Reasoning: An On-Policy Data Annotation Tool for Vision-Language Modelscited by 0