# Coreset Selection for Object Detection

arXiv: https://arxiv.org/abs/2404.09161 · HTML: https://mlsanity.com/paper/2404.09161/

## Key facts

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

## Builds on (most cited first, 25 of 25)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Learning Transferable Visual Models From Natural Language Supervision](https://mlsanity.com/paper/2103.00020/) (2021)
- [Microsoft COCO: Common Objects in Context](https://mlsanity.com/paper/1405.0312/) (2014)
- [Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks](https://mlsanity.com/paper/1506.01497/) (2015)
- [End-to-End Object Detection with Transformers](https://mlsanity.com/paper/2005.12872/) (2020)
- [YOLOv3: An Incremental Improvement](https://mlsanity.com/paper/1804.02767/) (2018)
- [Masked-attention Mask Transformer for Universal Image Segmentation](https://mlsanity.com/paper/2112.01527/) (2021)
- [FCOS: Fully Convolutional One-Stage Object Detection](https://mlsanity.com/paper/1904.01355/) (2019)
- [BDD100K: A Diverse Driving Dataset for Heterogeneous Multitask Learning](https://mlsanity.com/paper/1805.04687/) (2018)
- [Dataset Distillation by Matching Training Trajectories](https://mlsanity.com/paper/2203.11932/) (2022)
- [Selection via Proxy: Efficient Data Selection for Deep Learning](https://mlsanity.com/paper/1906.11829/) (2019)
- [Frustratingly Simple Few-Shot Object Detection](https://mlsanity.com/paper/2003.06957/) (2020)
- [Dataset Distillation with Infinitely Wide Convolutional Networks](https://mlsanity.com/paper/2107.13034/) (2021)
- [Dataset Distillation using Neural Feature Regression](https://mlsanity.com/paper/2206.00719/) (2022)
- [DeepCore: A Comprehensive Library for Coreset Selection in Deep Learning](https://mlsanity.com/paper/2204.08499/) (2022)
- [DSSD : Deconvolutional Single Shot Detector](https://mlsanity.com/paper/1701.06659/) (2017)
- [Privacy for Free: How does Dataset Condensation Help Privacy?](https://mlsanity.com/paper/2206.00240/) (2022)
- [Imbalance Problems in Object Detection: A Review](https://mlsanity.com/paper/1909.00169/) (2019)
- [Submodular Combinatorial Information Measures with Applications in Machine Learning](https://mlsanity.com/paper/2006.15412/) (2020)
- [Multiple instance active learning for object detection](https://mlsanity.com/paper/2104.02324/) (2021)
- [Coresets for Clustering with Fairness Constraints](https://mlsanity.com/paper/1906.08484/) (2019)
- [Wanderlust: Online Continual Object Detection in the Real World](https://mlsanity.com/paper/2108.11005/) (2021)
- [TALISMAN: Targeted Active Learning for Object Detection with Rare Classes and Slices using Submodular Mutual Information](https://mlsanity.com/paper/2112.00166/) (2021)
- [Coresets for Ordered Weighted Clustering](https://mlsanity.com/paper/1903.04351/) (2019)
- [On Coresets for Clustering in Small Dimensional Euclidean Spaces](https://mlsanity.com/paper/2302.13737/) (2023)

## Built on by (most cited first, 18 of 18)

- [A Coreset Selection of Coreset Selection Literature: Introduction and Recent Advances](https://mlsanity.com/paper/2505.17799/) (2025)
- [Speculative Coreset Selection for Task-Specific Fine-tuning](https://mlsanity.com/paper/2410.01296/) (2024)
- [UPCORE: Utility-Preserving Coreset Selection for Balanced Unlearning](https://mlsanity.com/paper/2502.15082/) (2025)
- [OD3: Optimization-free Dataset Distillation for Object Detection](https://mlsanity.com/paper/2506.01942/) (2025)
- [AdaDeDup: Adaptive Hybrid Data Pruning for Efficient Large-Scale Object Detection Training](https://mlsanity.com/paper/2507.00049/) (2025)
- [VideoCompressa: Data-Efficient Video Understanding via Joint Temporal Compression and Spatial Reconstruction](https://mlsanity.com/paper/2511.18831/) (2025)
- [Class-Proportional Coreset Selection for Difficulty-Separable Data](https://mlsanity.com/paper/2507.10904/) (2025)
- [DUSE: A Data Expansion Framework for Low-resource Automatic Modulation Recognition based on Active Learning](https://mlsanity.com/paper/2507.12011/) (2025)
- [Accelerating Benchmarking of Functional Connectivity Modeling via Structure-aware Core-set Selection](https://mlsanity.com/paper/2602.05667/) (2026)
- [Extending Dataset Pruning to Object Detection: A Variance-based Approach](https://mlsanity.com/paper/2505.17245/) (2025)
- [FoQuS: A Forgetting-Quality Coreset Selection Framework for Automatic Modulation Recognition](https://mlsanity.com/paper/2509.08300/) (2025)
- [Data-Efficient Stream-Based Active Distillation for Scalable Edge Model Deployment](https://mlsanity.com/paper/2509.20484/) (2025)
- [Online Data Curation for Object Detection via Marginal Contributions to Dataset-level Average Precision](https://mlsanity.com/paper/2511.14197/) (2025)
- [Omnimodal Dataset Distillation via High-order Proxy Alignment](https://mlsanity.com/paper/2604.10666/) (2026)
- [Foundation-Assisted Active Learning for Object Detection Annotation](https://mlsanity.com/paper/2607.16671/) (2026)
- [Dataset Distillation Based on Saliency-Driven Prototype Alignment](https://mlsanity.com/paper/2607.25318/) (2026)
- [FuDU: A Fuzzy Dual-dimensional Uncertainty Framework for Streaming Active Learning in Industrial Defect Detection](https://mlsanity.com/paper/2609.02212/) (2026)
- [Meta-Learning for Data-Efficient Plant Growth Estimation via Vision Transformers and Fuzzy Clustering](https://mlsanity.com/paper/2609.10749/) (2026)

## 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.