# Region-level Active Detector Learning

arXiv: https://arxiv.org/abs/2108.09186 · HTML: https://mlsanity.com/paper/2108.09186/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 25 references that are in the map.
- First posted on arXiv 20 Aug 2021.

## Builds on (most cited first, 13 of 13)

- [Microsoft COCO: Common Objects in Context](https://mlsanity.com/paper/1405.0312/) (2014)
- [Deep Batch Active Learning by Diverse, Uncertain Gradient Lower Bounds](https://mlsanity.com/paper/1906.03671/) (2019)
- [Variational Adversarial Active Learning](https://mlsanity.com/paper/1904.00370/) (2019)
- [Cost-Effective Active Learning for Deep Image Classification](https://mlsanity.com/paper/1701.03551/) (2017)
- [Suggestive Annotation: A Deep Active Learning Framework for Biomedical Image Segmentation](https://mlsanity.com/paper/1706.04737/) (2017)
- [xView: Objects in Context in Overhead Imagery](https://mlsanity.com/paper/1802.07856/) (2018)
- [Active Learning for Deep Detection Neural Networks](https://mlsanity.com/paper/1911.09168/) (2019)
- [Active Learning for Deep Object Detection](https://mlsanity.com/paper/1809.09875/) (2018)
- [Localization-Aware Active Learning for Object Detection](https://mlsanity.com/paper/1801.05124/) (2018)
- [Cost-Effective Active Learning for Melanoma Segmentation](https://mlsanity.com/paper/1711.09168/) (2017)
- [Towards Human-Machine Cooperation: Self-supervised Sample Mining for Object Detection](https://mlsanity.com/paper/1803.09867/) (2018)
- [An Adaptive Supervision Framework for Active Learning in Object Detection](https://mlsanity.com/paper/1908.02454/) (2019)
- [Minimax Active Learning](https://mlsanity.com/paper/2012.10467/) (2020)

## Built on by (most cited first, 2 of 2)

- [MUS-CDB: Mixed Uncertainty Sampling with Class Distribution Balancing for Active Annotation in Aerial Object Detection](https://mlsanity.com/paper/2212.02804/) (2022)
- [MuRAL: Multi-Scale Region-based Active Learning for Object Detection](https://mlsanity.com/paper/2303.16637/) (2023)

## Abstract (arXiv)

Active learning for object detection is conventionally achieved by applying techniques developed for classification in a way that aggregates individual detections into image-level selection criteria. This is typically coupled with the costly assumption that every image selected for labelling must be exhaustively annotated. This yields incremental improvements on well-curated vision datasets and struggles in the presence of data imbalance and visual clutter that occurs in real-world imagery. Alternatives to the image-level approach are surprisingly under-explored in the literature. In this work, we introduce a new strategy that subsumes previous Image-level and Object-level approaches into a generalized, Region-level approach that promotes spatial-diversity by avoiding nearby redundant queries from the same image and minimizes context-switching for the labeler. We show that this approach significantly decreases labeling effort and improves rare object search on realistic data with inherent class-imbalance and cluttered scenes.