# Active Learning for Deep Object Detection

arXiv: https://arxiv.org/abs/1809.09875 · HTML: https://mlsanity.com/paper/1809.09875/

## Key facts

- Cited by 50 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 26 Sep 2018.

## Built on by (most cited first, 30 of 50)

- [Entropy-based Active Learning for Object Detection with Progressive Diversity Constraint](https://mlsanity.com/paper/2204.07965/) (2022)
- [Automatic Data Curation for Self-Supervised Learning: A Clustering-Based Approach](https://mlsanity.com/paper/2405.15613/) (2024)
- [CitySim: A Drone-Based Vehicle Trajectory Dataset for Safety Oriented Research and Digital Twins](https://mlsanity.com/paper/2208.11036/) (2022)
- [A Survey on Intrinsic Images: Delving Deep Into Lambert and Beyond](https://mlsanity.com/paper/2112.03842/) (2021)
- [Inspect, Understand, Overcome: A Survey of Practical Methods for AI Safety](https://mlsanity.com/paper/2104.14235/) (2021)
- [TALISMAN: Targeted Active Learning for Object Detection with Rare Classes and Slices using Submodular Mutual Information](https://mlsanity.com/paper/2112.00166/) (2021)
- [Streaming Active Learning with Deep Neural Networks](https://mlsanity.com/paper/2303.02535/) (2023)
- [Active Learning Strategies for Weakly-supervised Object Detection](https://mlsanity.com/paper/2207.12112/) (2022)
- [Exploring Diversity-based Active Learning for 3D Object Detection in Autonomous Driving](https://mlsanity.com/paper/2205.07708/) (2022)
- [Identifying Label Errors in Object Detection Datasets by Loss Inspection](https://mlsanity.com/paper/2303.06999/) (2023)
- [Multi-Task Consistency for Active Learning](https://mlsanity.com/paper/2306.12398/) (2023)
- [Active Learning for Semantic Segmentation with Multi-class Label Query](https://mlsanity.com/paper/2309.09319/) (2023)
- [ALBench: A Framework for Evaluating Active Learning in Object Detection](https://mlsanity.com/paper/2207.13339/) (2022)
- [Deep Active Learning for Computer Vision: Past and Future](https://mlsanity.com/paper/2211.14819/) (2022)
- [When Deep Learners Change Their Mind: Learning Dynamics for Active Learning](https://mlsanity.com/paper/2107.14707/) (2021)
- [You Never Get a Second Chance To Make a Good First Impression: Seeding Active Learning for 3D Semantic Segmentation](https://mlsanity.com/paper/2304.11762/) (2023)
- [Inconsistency-Based Data-Centric Active Open-Set Annotation](https://mlsanity.com/paper/2401.04923/) (2024)
- [An Expanded Benchmark that Rediscovers and Affirms the Edge of Uncertainty Sampling for Active Learning in Tabular Datasets](https://mlsanity.com/paper/2306.08954/) (2023)
- [Active Learning for Object Detection with Non-Redundant Informative Sampling](https://mlsanity.com/paper/2307.08414/) (2023)
- [Towards Rapid Prototyping and Comparability in Active Learning for Deep Object Detection](https://mlsanity.com/paper/2212.10836/) (2022)
- [Assessing Domain Gap for Continual Domain Adaptation in Object Detection](https://mlsanity.com/paper/2302.10396/) (2023)
- [Deep Active Learning with Noisy Oracle in Object Detection](https://mlsanity.com/paper/2310.00372/) (2023)
- [Streamlining the Development of Active Learning Methods in Real-World Object Detection](https://mlsanity.com/paper/2508.19906/) (2025)
- [Region-level Active Detector Learning](https://mlsanity.com/paper/2108.09186/) (2021)
- [Portable Active Learning for Object Detection](https://mlsanity.com/paper/2605.10349/) (2026)
- [Evaluating Zero-cost Active Learning for Object Detection](https://mlsanity.com/paper/2212.04211/) (2022)
- [Classification Committee for Active Deep Object Detection](https://mlsanity.com/paper/2308.08476/) (2023)
- [Two-Step Active Learning for Instance Segmentation with Uncertainty and Diversity Sampling](https://mlsanity.com/paper/2309.16139/) (2023)
- [Reinforcement-based Display-size Selection for Frugal Satellite Image Change Detection](https://mlsanity.com/paper/2312.16965/) (2023)
- [Improved detection of discarded fish species through BoxAL active learning](https://mlsanity.com/paper/2410.04880/) (2024)

## Abstract (arXiv)

The great success that deep models have achieved in the past is mainly owed to large amounts of labeled training data. However, the acquisition of labeled data for new tasks aside from existing benchmarks is both challenging and costly. Active learning can make the process of labeling new data more efficient by selecting unlabeled samples which, when labeled, are expected to improve the model the most. In this paper, we combine a novel method of active learning for object detection with an incremental learning scheme to enable continuous exploration of new unlabeled datasets. We propose a set of uncertainty-based active learning metrics suitable for most object detectors. Furthermore, we present an approach to leverage class imbalances during sample selection. All methods are evaluated systematically in a continuous exploration context on the PASCAL VOC 2012 dataset.