mlsanityData to 7 Oct 2026

Coreset Selection for Object Detection

arXiv 2404.09161 ↗First posted 14 Apr 2024cs.CV cs.LGcited by 18

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Abstract (arXiv)

Coreset selection is a method for selecting a small, representative subset of an entire dataset. It has been primarily researched in image classification, assuming there is only one object per image. However, coreset selection for object detection is more challenging as an image can contain multiple objects. As a result, much research has yet to be done on this topic. Therefore, we introduce a new approach, Coreset Selection for Object Detection (CSOD). CSOD generates imagewise and classwise representative feature vectors for multiple objects of the same class within each image. Subsequently, we adopt submodular optimization for considering both representativeness and diversity and utilize the representative vectors in the submodular optimization process to select a subset. When we evaluated CSOD on the Pascal VOC dataset, CSOD outperformed random selection by +6.4%p in AP50 when selecting 200 images.

Key facts. Cited by 18 papers in the map (data through 7 Oct 2026). Builds on 25 of its 33 references that are in the map. First posted on arXiv 14 Apr 2024.

Builds on

25 of 33 references.

References · 25

  1. 2015Deep Residual Learning for Image Recognitioncited by 51,809
  2. 2021Learning Transferable Visual Models From Natural Language Supervisioncited by 33,456
  3. 2014Microsoft COCO: Common Objects in Contextcited by 17,498
  4. 2015Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networkscited by 9,617
  5. 2020End-to-End Object Detection with Transformerscited by 7,384
  6. 2018YOLOv3: An Incremental Improvementcited by 2,652
  7. 2021Masked-attention Mask Transformer for Universal Image Segmentationcited by 2,338
  8. 2019FCOS: Fully Convolutional One-Stage Object Detectioncited by 1,573
  9. 2018BDD100K: A Diverse Driving Dataset for Heterogeneous Multitask Learningcited by 1,282
  10. 2022Dataset Distillation by Matching Training Trajectoriescited by 437
  11. 2019Selection via Proxy: Efficient Data Selection for Deep Learningcited by 337
  12. 2020Frustratingly Simple Few-Shot Object Detectioncited by 280
13 more
  1. 2021Dataset Distillation with Infinitely Wide Convolutional Networkscited by 215
  2. 2022Dataset Distillation using Neural Feature Regressioncited by 177
  3. 2022DeepCore: A Comprehensive Library for Coreset Selection in Deep Learningcited by 146
  4. 2017DSSD : Deconvolutional Single Shot Detectorcited by 130
  5. 2022Privacy for Free: How does Dataset Condensation Help Privacy?cited by 119
  6. 2019Imbalance Problems in Object Detection: A Reviewcited by 118
  7. 2020Submodular Combinatorial Information Measures with Applications in Machine Learningcited by 88
  8. 2021Multiple instance active learning for object detectioncited by 77
  9. 2019Coresets for Clustering with Fairness Constraintscited by 51
  10. 2021Wanderlust: Online Continual Object Detection in the Real Worldcited by 34
  11. 2021TALISMAN: Targeted Active Learning for Object Detection with Rare Classes and Slices using Submodular Mutual Informationcited by 27
  12. 2019Coresets for Ordered Weighted Clusteringcited by 8
  13. 2023On Coresets for Clustering in Small Dimensional Euclidean Spacescited by 3

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 1; 2025: 11; 2026: 6.

18 papers.

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  1. 2025A Coreset Selection of Coreset Selection Literature: Introduction and Recent Advancescited by 28
  2. 2024Speculative Coreset Selection for Task-Specific Fine-tuningcited by 10
  3. 2025UPCORE: Utility-Preserving Coreset Selection for Balanced Unlearningcited by 7
  4. 2025OD3: Optimization-free Dataset Distillation for Object Detectioncited by 3
  5. 2025AdaDeDup: Adaptive Hybrid Data Pruning for Efficient Large-Scale Object Detection Trainingcited by 3
  6. 2025Class-Proportional Coreset Selection for Difficulty-Separable Datacited by 2
  7. 2025DUSE: A Data Expansion Framework for Low-resource Automatic Modulation Recognition based on Active Learningcited by 2
  8. 2025VideoCompressa: Data-Efficient Video Understanding via Joint Temporal Compression and Spatial Reconstructioncited by 2
  9. 2026Accelerating Benchmarking of Functional Connectivity Modeling via Structure-aware Core-set Selectioncited by 1
  10. 2025Extending Dataset Pruning to Object Detection: A Variance-based Approachcited by 0
  11. 2025FoQuS: A Forgetting-Quality Coreset Selection Framework for Automatic Modulation Recognitioncited by 0
  12. 2025Data-Efficient Stream-Based Active Distillation for Scalable Edge Model Deploymentcited by 0
6 more
  1. 2025Online Data Curation for Object Detection via Marginal Contributions to Dataset-level Average Precisioncited by 0
  2. 2026Omnimodal Dataset Distillation via High-order Proxy Alignmentcited by 0
  3. 2026Foundation-Assisted Active Learning for Object Detection Annotationcited by 0
  4. 2026Dataset Distillation Based on Saliency-Driven Prototype Alignmentcited by 0
  5. 2026FuDU: A Fuzzy Dual-dimensional Uncertainty Framework for Streaming Active Learning in Industrial Defect Detectioncited by 0
  6. 2026Meta-Learning for Data-Efficient Plant Growth Estimation via Vision Transformers and Fuzzy Clusteringcited by 0