# Robust Object Detection With Inaccurate Bounding Boxes

arXiv: https://arxiv.org/abs/2207.09697 · HTML: https://mlsanity.com/paper/2207.09697/

## Key facts

- Cited by 20 papers in the map (data through 14 Oct 2026).
- Builds on 28 of its 51 references that are in the map.
- First posted on arXiv 20 Jul 2022.

## Builds on (most cited first, 28 of 28)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [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)
- [Feature Pyramid Networks for Object Detection](https://mlsanity.com/paper/1612.03144/) (2016)
- [MMDetection: Open MMLab Detection Toolbox and Benchmark](https://mlsanity.com/paper/1906.07155/) (2019)
- [Cascade R-CNN: Delving into High Quality Object Detection](https://mlsanity.com/paper/1712.00726/) (2017)
- [Co-teaching: Robust Training of Deep Neural Networks with Extremely Noisy Labels](https://mlsanity.com/paper/1804.06872/) (2018)
- [Generalized Cross Entropy Loss for Training Deep Neural Networks with Noisy Labels](https://mlsanity.com/paper/1805.07836/) (2018)
- [MentorNet: Learning Data-Driven Curriculum for Very Deep Neural Networks on Corrupted Labels](https://mlsanity.com/paper/1712.05055/) (2017)
- [The Open Images Dataset V4: Unified image classification, object detection, and visual relationship detection at scale](https://mlsanity.com/paper/1811.00982/) (2018)
- [Robust Loss Functions under Label Noise for Deep Neural Networks](https://mlsanity.com/paper/1712.09482/) (2017)
- [Weakly Supervised Deep Detection Networks](https://mlsanity.com/paper/1511.02853/) (2015)
- [Dimensionality-Driven Learning with Noisy Labels](https://mlsanity.com/paper/1806.02612/) (2018)
- [Bounding Box Regression with Uncertainty for Accurate Object Detection](https://mlsanity.com/paper/1809.08545/) (2018)
- [Multiple Instance Detection Network with Online Instance Classifier Refinement](https://mlsanity.com/paper/1704.00138/) (2017)
- [FreeAnchor: Learning to Match Anchors for Visual Object Detection](https://mlsanity.com/paper/1909.02466/) (2019)
- [C-MIL: Continuation Multiple Instance Learning for Weakly Supervised Object Detection](https://mlsanity.com/paper/1904.05647/) (2019)
- [Weakly Supervised Cascaded Convolutional Networks](https://mlsanity.com/paper/1611.08258/) (2016)
- [Min-Entropy Latent Model for Weakly Supervised Object Detection](https://mlsanity.com/paper/1902.06057/) (2019)
- [Global Wheat Head Detection (GWHD) dataset: a large and diverse dataset of high resolution RGB labelled images to develop and benchmark wheat head detection methods](https://mlsanity.com/paper/2005.02162/) (2020)
- [ContextLocNet: Context-Aware Deep Network Models for Weakly Supervised Localization](https://mlsanity.com/paper/1609.04331/) (2016)
- [NOTE-RCNN: NOise Tolerant Ensemble RCNN for Semi-Supervised Object Detection](https://mlsanity.com/paper/1812.00124/) (2018)
- [Revisiting knowledge transfer for training object class detectors](https://mlsanity.com/paper/1708.06128/) (2017)
- [Towards Noise-resistant Object Detection with Noisy Annotations](https://mlsanity.com/paper/2003.01285/) (2020)
- [Visual and Semantic Knowledge Transfer for Large Scale Semi-supervised Object Detection](https://mlsanity.com/paper/1801.03145/) (2018)
- [TS2C: Tight Box Mining with Surrounding Segmentation Context for Weakly Supervised Object Detection](https://mlsanity.com/paper/1807.04897/) (2018)
- [Training Object Detectors With Noisy Data](https://mlsanity.com/paper/1905.07202/) (2019)
- [Weakly-supervised Discovery of Visual Pattern Configurations](https://mlsanity.com/paper/1406.6507/) (2014)

## Built on by (most cited first, 20 of 20)

- [Point-Query Quadtree for Crowd Counting, Localization, and More](https://mlsanity.com/paper/2308.13814/) (2023)
- [Spatial Self-Distillation for Object Detection with Inaccurate Bounding Boxes](https://mlsanity.com/paper/2307.12101/) (2023)
- [Label Convergence: Defining an Upper Performance Bound in Object Recognition through Contradictory Annotations](https://mlsanity.com/paper/2409.09412/) (2024)
- [Embrace Limited and Imperfect Training Datasets: Opportunities and Challenges in Plant Disease Recognition Using Deep Learning](https://mlsanity.com/paper/2305.11533/) (2023)
- [ACT-Net: Anchor-context Action Detection in Surgery Videos](https://mlsanity.com/paper/2310.03377/) (2023)
- [Sample Selection via Contrastive Fragmentation for Noisy Label Regression](https://mlsanity.com/paper/2502.17771/) (2025)
- [Distribution-Aware Calibration for Object Detection with Noisy Bounding Boxes](https://mlsanity.com/paper/2308.12017/) (2023)
- [Universal Noise Annotation: Unveiling the Impact of Noisy annotation on Object Detection](https://mlsanity.com/paper/2312.13822/) (2023)
- [NBBOX: Noisy Bounding Box Improves Remote Sensing Object Detection](https://mlsanity.com/paper/2409.09424/) (2024)
- [Dynamic Loss Decay based Robust Oriented Object Detection on Remote Sensing Images with Noisy Labels](https://mlsanity.com/paper/2405.09024/) (2024)
- [RowDetr: End-to-End Crop Row Detection Using Polynomials](https://mlsanity.com/paper/2412.10525/) (2024)
- [Mitigating the Impact of Prominent Position Shift in Drone-based RGBT Object Detection](https://mlsanity.com/paper/2502.09311/) (2025)
- [Pseudo-Labeling Driven Refinement of Benchmark Object Detection Datasets via Analysis of Learning Patterns](https://mlsanity.com/paper/2506.00997/) (2025)
- [Knowledge-Guided Adversarial Training for Infrared Object Detection via Thermal Radiation Modeling](https://mlsanity.com/paper/2603.25170/) (2026)
- [Generalized Small Object Detection:A Point-Prompted Paradigm and Benchmark](https://mlsanity.com/paper/2604.02773/) (2026)
- [FMG-Det: Foundation Model Guided Robust Object Detection](https://mlsanity.com/paper/2505.23726/) (2025)
- [Unlocking Robust Semantic Segmentation Performance via Label-only Elastic Deformations against Implicit Label Noise](https://mlsanity.com/paper/2508.10383/) (2025)
- [Noise-Robust Tiny Object Localization with Flows](https://mlsanity.com/paper/2601.00617/) (2026)
- [Active Label Cleaning for Reliable Detection of Electron Dense Deposits in Transmission Electron Microscopy Images](https://mlsanity.com/paper/2602.05250/) (2026)
- [Consistency Beyond Contrast: Enhancing Open-Vocabulary Object Detection Robustness via Contextual Consistency Learning](https://mlsanity.com/paper/2603.26179/) (2026)

## Abstract (arXiv)

Learning accurate object detectors often requires large-scale training data with precise object bounding boxes. However, labeling such data is expensive and time-consuming. As the crowd-sourcing labeling process and the ambiguities of the objects may raise noisy bounding box annotations, the object detectors will suffer from the degenerated training data. In this work, we aim to address the challenge of learning robust object detectors with inaccurate bounding boxes. Inspired by the fact that localization precision suffers significantly from inaccurate bounding boxes while classification accuracy is less affected, we propose leveraging classification as a guidance signal for refining localization results. Specifically, by treating an object as a bag of instances, we introduce an Object-Aware Multiple Instance Learning approach (OA-MIL), featured with object-aware instance selection and object-aware instance extension. The former aims to select accurate instances for training, instead of directly using inaccurate box annotations. The latter focuses on generating high-quality instances for selection. Extensive experiments on synthetic noisy datasets (i.e., noisy PASCAL VOC and MS-COCO) and a real noisy wheat head dataset demonstrate the effectiveness of our OA-MIL. Code is available at https://github.com/cxliu0/OA-MIL.