mlsanityData to 14 Oct 2026

Online Data Curation for Object Detection via Marginal Contributions to Dataset-level Average Precision

arXiv 2511.14197 ↗First posted 18 Nov 2025cs.CVcited by 0

On the map: Knowledge distillation → Detection and dense prediction distillation

Abstract (arXiv)

High-quality data has become a primary driver of progress under scale laws, with curated datasets often outperforming much larger unfiltered ones at lower cost. Online data curation extends this idea by dynamically selecting training samples based on the model's evolving state. While effective in classification and multimodal learning, existing online sampling strategies rarely extend to object detection because of its structural complexity and domain gaps. We introduce DetGain, an online data curation method specifically for object detection that estimates the marginal perturbation of each image to dataset-level Average Precision (AP) based on its prediction quality. By modeling global score distributions, DetGain efficiently estimates the global AP change and computes teacher-student contribution gaps to select informative samples at each iteration. The method is architecture-agnostic and minimally intrusive, enabling straightforward integration into diverse object detection architectures. Experiments on the COCO dataset with multiple representative detectors show consistent improvements in accuracy. DetGain also demonstrates strong robustness under low-quality data and can be effectively combined with knowledge distillation techniques to further enhance performance, highlighting its potential as a general and complementary strategy for data-efficient object detection.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Placed in Detection and dense prediction distillation, one of 12 directions in Knowledge distillation. Builds on 51 of its 63 references that are in the map. First posted on arXiv 18 Nov 2025.

Builds on

51 of 63 references.

Closest ancestors · 4

Same direction, newest first

  1. 2023CrossKD: Cross-Head Knowledge Distillation for Object Detection20 Jun
  2. 2023Active Teacher for Semi-Supervised Object Detection15 Mar
  3. 2022PKD: General Distillation Framework for Object Detectors via Pearson Correlation Coefficient5 Jul
  4. 2021Localization Distillation for Dense Object Detection24 Feb

Other references · 47

  1. 2015Deep Residual Learning for Image Recognitioncited by 52,096
  2. 2014Microsoft COCO: Common Objects in Contextcited by 17,608
  3. 2021Swin Transformer: Hierarchical Vision Transformer using Shifted Windowscited by 12,139
  4. 2015Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networkscited by 9,639
  5. 2023DINOv2: Learning Robust Visual Features without Supervisioncited by 7,974
  6. 2020End-to-End Object Detection with Transformerscited by 7,414
  7. 2020Deformable DETR: Deformable Transformers for End-to-End Object Detectioncited by 3,422
  8. 2015SSD: Single Shot MultiBox Detectorcited by 3,130
  9. 2016Aggregated Residual Transformations for Deep Neural Networkscited by 2,546
  10. 2019FCOS: Fully Convolutional One-Stage Object Detectioncited by 1,577
  11. 2019MMDetection: Open MMLab Detection Toolbox and Benchmarkcited by 1,385
  12. 2022DINO: DETR with Improved DeNoising Anchor Boxes for End-to-End Object Detectioncited by 1,319
35 more
  1. 2018BDD100K: A Diverse Driving Dataset for Heterogeneous Multitask Learningcited by 1,288
  2. 2018Co-teaching: Robust Training of Deep Neural Networks with Extremely Noisy Labelscited by 1,059
  3. 2018An Empirical Study of Example Forgetting during Deep Neural Network Learningcited by 678
  4. 2017MentorNet: Learning Data-Driven Curriculum for Very Deep Neural Networks on Corrupted Labelscited by 653
  5. 2016Training Region-based Object Detectors with Online Hard Example Miningcited by 579
  6. 2023Textbooks Are All You Needcited by 569
  7. 2018Albumentations: fast and flexible image augmentationscited by 560
  8. 2019Bridging the Gap Between Anchor-based and Anchor-free Detection via Adaptive Training Sample Selectioncited by 540
  9. 2020Generalized Focal Loss: Learning Qualified and Distributed Bounding Boxes for Dense Object Detectioncited by 409
  10. 2018Deep Learning for Generic Object Detection: A Surveycited by 392
  11. 2019Selection via Proxy: Efficient Data Selection for Deep Learningcited by 339
  12. 2023SemDeDup: Data-efficient learning at web-scale through semantic deduplicationcited by 303
  13. 2021GRAD-MATCH: Gradient Matching based Data Subset Selection for Efficient Deep Model Trainingcited by 274
  14. 2022Prioritized Training on Points that are Learnable, Worth Learning, and Not Yet Learntcited by 236
  15. 2020VarifocalNet: An IoU-aware Dense Object Detectorcited by 231
  16. 2020Coresets via Bilevel Optimization for Continual Learning and Streamingcited by 192
  17. 2020Contextual Diversity for Active Learningcited by 139
  18. 2015Online Batch Selection for Faster Training of Neural Networkscited by 122
  19. 2024Data Shapley in One Training Runcited by 81
  20. 2021Active Learning for Deep Object Detection via Probabilistic Modelingcited by 79
  21. 2021Multiple instance active learning for object detectioncited by 77
  22. 2019Prime Sample Attention in Object Detectioncited by 55
  23. 2022Entropy-based Active Learning for Object Detection with Progressive Diversity Constraintcited by 53
  24. 2019Ordered SGD: A New Stochastic Optimization Framework for Empirical Risk Minimizationcited by 51
  25. 2018Localization-Aware Active Learning for Object Detectioncited by 48
  26. 2019Towards Accurate One-Stage Object Detection with AP-Losscited by 36
  27. 2024Data curation via joint example selection further accelerates multimodal learningcited by 33
  28. 2022Plug and Play Active Learning for Object Detectioncited by 28
  29. 2023Bad Students Make Great Teachers: Active Learning Accelerates Large-Scale Visual Understandingcited by 28
  30. 2020Choosing the Sample with Lowest Loss makes SGD Robustcited by 26
  31. 2021Robust and Decomposable Average Precision for Image Retrievalcited by 19
  32. 2024Active Data Curation Effectively Distills Large-Scale Multimodal Modelscited by 19
  33. 2024Coreset Selection for Object Detectioncited by 18
  34. 2023Learning Lightweight Object Detectors via Multi-Teacher Progressive Distillationcited by 10
  35. 2025Progressive Data Dropout: An Embarrassingly Simple Approach to Faster Trainingcited by 4

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