# Object Detection as a Positive-Unlabeled Problem

arXiv: https://arxiv.org/abs/2002.04672 · HTML: https://mlsanity.com/paper/2002.04672/

## Key facts

- Cited by 18 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 11 Feb 2020.

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

- [Sylph: A Hypernetwork Framework for Incremental Few-shot Object Detection](https://mlsanity.com/paper/2203.13903/) (2022)
- [Few-shot Learning with Noisy Labels](https://mlsanity.com/paper/2204.05494/) (2022)
- [SparseDet: Improving Sparsely Annotated Object Detection with Pseudo-positive Mining](https://mlsanity.com/paper/2201.04620/) (2022)
- [Calibrated Teacher for Sparsely Annotated Object Detection](https://mlsanity.com/paper/2303.07582/) (2023)
- [Positive-unlabeled Learning for Cell Detection in Histopathology Images with Incomplete Annotations](https://mlsanity.com/paper/2106.15918/) (2021)
- [Extending One-Stage Detection with Open-World Proposals](https://mlsanity.com/paper/2201.02302/) (2022)
- [Cell Detection from Imperfect Annotation by Pseudo Label Selection Using P-classification](https://mlsanity.com/paper/2107.09289/) (2021)
- [Universal Noise Annotation: Unveiling the Impact of Noisy annotation on Object Detection](https://mlsanity.com/paper/2312.13822/) (2023)
- [Dynamic Supervisor for Cross-dataset Object Detection](https://mlsanity.com/paper/2204.00183/) (2022)
- [Positive-unlabeled learning for binary and multi-class cell detection in histopathology images with incomplete annotations](https://mlsanity.com/paper/2302.08050/) (2023)
- [Tackling the Incomplete Annotation Issue in Universal Lesion Detection Task By Exploratory Training](https://mlsanity.com/paper/2309.13306/) (2023)
- [TagMe: GPS-Assisted Automatic Object Annotation in Videos](https://mlsanity.com/paper/2103.13428/) (2021)
- [Single Stage Class Agnostic Common Object Detection: A Simple Baseline](https://mlsanity.com/paper/2104.12245/) (2021)
- [Weakly-Supervised Semantic Segmentation of Ships Using Thermal Imagery](https://mlsanity.com/paper/2212.13170/) (2022)
- [Mitosis Detection from Partial Annotation by Dataset Generation via Frame-Order Flipping](https://mlsanity.com/paper/2307.04113/) (2023)
- [XAI-guided Insulator Anomaly Detection for Imbalanced Datasets](https://mlsanity.com/paper/2409.16821/) (2024)
- [RoNFA: Robust Neural Field-based Approach for Few-Shot Image Classification with Noisy Labels](https://mlsanity.com/paper/2506.03461/) (2025)
- [Background-Free Objectness Learning for Class-Agnostic Detection](https://mlsanity.com/paper/2608.29232/) (2026)

## Abstract (arXiv)

As with other deep learning methods, label quality is important for learning modern convolutional object detectors. However, the potentially large number and wide diversity of object instances that can be found in complex image scenes makes constituting complete annotations a challenging task; objects missing annotations can be observed in a variety of popular object detection datasets. These missing annotations can be problematic, as the standard cross-entropy loss employed to train object detection models treats classification as a positive-negative (PN) problem: unlabeled regions are implicitly assumed to be background. As such, any object missing a bounding box results in a confusing learning signal, the effects of which we observe empirically. To remedy this, we propose treating object detection as a positive-unlabeled (PU) problem, which removes the assumption that unlabeled regions must be negative. We demonstrate that our proposed PU classification loss outperforms the standard PN loss on PASCAL VOC and MS COCO across a range of label missingness, as well as on Visual Genome and DeepLesion with full labels.