Training Object Detectors With Noisy Data
Open accessAbstract
The availability of a large quantity of labelled training data is crucial for the training of modern object detectors. Hand labelling training data is time consuming and expensive while automatic labelling methods inevitably add unwanted noise to the labels. We examine the effect of different types of label noise on the performance of an object detector. We then show how co-teaching, a method developed for handling noisy labels and previously demonstrated on a classification problem, can be improved to mitigate the effects of label noise in an object detection setting. We illustrate our results using simulated noise on the KITTI dataset and on a vehicle detection task using automatically labelled data.
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Cites
Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks
Ren, S., He, K., Girshick, R., & Sun, J. (2015) · arXiv · doi:10.48550/arxiv.1506.01497 · Cited by 70,812 · see citing works
You Only Look Once: Unified, Real-Time Object Detection
Redmon, J., Divvala, S., Girshick, R., & Farhadi, A. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.91 · Cited by 53,803 · see citing works
ImageNet Large Scale Visual Recognition Challenge
Russakovsky, O., Deng, J., Su, H., Krause, J., Satheesh, S., Ma, S., Huang, Z., Karpathy, A., Khosla, A., Bernstein, M., et al. (2015) · International Journal of Computer Vision · doi:10.1007/s11263-015-0816-y · Cited by 39,380 · see citing works
(2016) · Cited by 31,590 · see citing works
Focal Loss for Dense Object Detection
Lin, T.-Y., Goyal, P., Girshick, R., He, K., & Dollar, P. (2017) · 2017 IEEE International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2017.324 · Cited by 30,581 · see citing works
Are we ready for autonomous driving? The KITTI vision benchmark suite
Geiger, A., Lenz, P., & Urtasun, R. (2012) · 2012 IEEE Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2012.6248074 · Cited by 14,897 · see citing works
Identity mappings in deep residual networks
He, K., Zhang, X., Ren, S., & Sun, J. (2016) · Lecture Notes in Computer Science · doi:10.1007/978-3-319-46493-0_38 · Cited by 10,741 · see citing works
Combining labeled and unlabeled data with co-training
Blum, A. L., & Mitchell, T. M. (1998) · Conference on Learning Theory · doi:10.1145/279943.279962 · Cited by 5,597 · see citing works
Co-teaching: Robust Training of Deep Neural Networks with Extremely Noisy LabelsPreprint
Han, B., Yao, Q., Yu, X., Niu, G., Xu, M., Hu, W., Tsang, I. W.-H., & Sugiyama, M. (2018) · arXiv (Cornell University) · doi:10.5555/3327757.3327944 · Cited by 1,674 · see citing works
A Closer Look at Memorization in Deep Networks
Arpit, D., Jastrzębski, S., Ballas, N., Krueger, D., Bengio, E., Kanwal, M. S., Maharaj, T., Fischer, A., Courville, A., Bengio, Y., et al. (2017) · arXiv · doi:10.48550/arxiv.1706.05394 · Cited by 1,307 · see citing works
Exploring the Limits of Weakly Supervised Pretraining
Mahajan, D., Girshick, R., Ramanathan, V., He, K., Paluri, M., Li, Y., Bharambe, A., & van der Maaten, L. (2018) · Lecture Notes in Computer Science · doi:10.1007/978-3-030-01216-8_12 · Cited by 1,224 · see citing works
Learning to Reweight Examples for Robust Deep LearningPreprint
Ren, M., Zeng, W., Yang, B., & Urtasun, R. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1803.09050 · Cited by 1,091 · see citing works
Semi-Supervised Learning with Ladder NetworksPreprint
Rasmus, A., Harri Valpola, B., Honkala, M., Berglund, M., & Raiko, T. (2015) · arXiv (Cornell University) · doi:10.48550/arxiv.1507.02672 · Cited by 941 · see citing works
Training Deep Networks with Synthetic Data: Bridging the Reality Gap by Domain Randomization
Tremblay, J., Prakash, A., Acuna, D., Brophy, M., Jampani, V., Anil, C., To, T., Cameracci, E., Boochoon, S., & Birchfield, S. T. (2018) · IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw.2018.00143 · Cited by 936 · see citing works
Semi-Supervised Self-Training of Object Detection Models
Rosenberg, C., Hebert, M., & Schneiderman, H. (2005) · 2005 Seventh IEEE Workshops on Applications of Computer Vision (WACV/MOTION'05) - Volume 1 · doi:10.1109/acvmot.2005.107 · Cited by 796 · see citing works
Training Deep Neural Networks on Noisy Labels with BootstrappingPreprint
Reed, S., Lee, H., Anguelov, D., et al. (2014) · arXiv (Cornell University) · doi:10.48550/arxiv.1412.6596 · Cited by 762 · see citing works
Training Convolutional Networks with Noisy LabelsPreprint
Sukhbaatar, S., Bruna, J., Paluri, M., Bourdev, L. D., & Fergus, R. (2014) · arXiv (Cornell University) · doi:10.48550/arxiv.1406.2080 · Cited by 532 · see citing works
Training deep neural-networks using a noise adaptation layer
Goldberger, J., & Ben-Reuven, E. (2017) · International Conference on Learning Representations · Cited by 511 · see citing works
The Unreasonable Effectiveness of Noisy Data for Fine-Grained Recognition
Krause, J., Sapp, B., Howard, A., Zhou, H., Toshev, A., Duerig, T., Philbin, J. F., & Fei-Fei, L. (2016) · Lecture notes in computer science · doi:10.1007/978-3-319-46487-9_19 · Cited by 311 · see citing works
Distant Vehicle Detection Using Radar and Vision
Chadwick, S., Maddern, W., & Newman, P. M. (2019) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra.2019.8794312 · Cited by 237 · see citing works
Self-citationDetecting Traffic Lights by Single Shot Detection
Muller, J., & Dietmayer, K. (2018) · IEEE International Conference on Intelligent Transportation Systems (ITSC) · doi:10.1109/itsc.2018.8569683 · Cited by 110 · see citing works
Watch and learn: Semi-supervised learning of object detectors from videos
Misra, I., Shrivastava, A., & Hebert, M. (2015) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2015.7298982 · Cited by 108 · see citing works
Unsupervised Hard Example Mining from Videos for Improved Object Detection
Jin, S., RoyChowdhury, A., Jiang, H., Singh, A., Prasad, A., Chakraborty, D., & Miller, E. G. (2018) · Lecture notes in computer science · doi:10.1007/978-3-030-01261-8_19 · Cited by 74 · see citing works
Soft Sampling for Robust Object DetectionPreprint
Wu, Z., Bodla, N., Singh, B., Najibi, M., Chellappa, R., & Davis, L. S. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1806.06986 · Cited by 55 · see citing works
Mark Yourself: Road Marking Segmentation via Weakly-Supervised Annotations from Multimodal Data
Bruls, T., Maddern, W., Morye, A. A., & Newman, P. M. (2018) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra.2018.8460952 · Cited by 38 · see citing works
Self-citationExperience based navigation : theory, practice and implementation
Churchill, W. S., & Churchill, W. S. (2012) · Oxford University Research Archive (ORA) (University of Oxford) · doi:10.5287/ora-dpmw4xkoa · Cited by 29 · see citing works
Detecting Traffic Lights by Single Shot DetectionPreprint
Müller, J., & Dietmayer, K. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1805.02523
Flagged first, then by citations
Research works printed but not linked: 1
- M. Everingham, L. Van Gool, C. K. I. Williams, J. Winn, and A. Zisserman, "The PASCAL Visual Object Classes Challenge 2012 (VOC2012) Results," http://www.pascal- network.org/challenges/VOC/voc2012/workshop/index.html.
Cited by
Sorted by citations
Feng, D., Haase-Schutz, C., Rosenbaum, L., Hertlein, H., Glaser, C., Timm, F., Wiesbeck, W., & Dietmayer, K. (2021) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2020.2972974 · Cited by 1,425 · see citing works
- Authors
- Chadwick, S., & Newman, P.
- Affiliations
- University of Oxford (GB)
- ORCID
- Newman, Paul
- Venue
- 2019 IEEE Intelligent Vehicles Symposium (IV)
- Publisher
- IEEE
- Published
- 2019-06
- Pages
- 1319-1325
- ISSN
- 1931-0587 (ISSN-L)
- OpenAlex
- W2946804865
- Cited by
- 46
- References
- 27
- Citations by year
- 2026: 4 · 2025: 7 · 2024: 10 · 2023: 7 · 2022: 3 · 2021: 6 · 2020: 9
- FWCI
- 1.72
- Type
- conference paper
- Language
- en
- Open access
- green
- arxiv.org
- Locations
- IEEE Intelligent Vehicles Symposium (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University)
- IV
- Funders
- Engineering and Physical Sciences Research Council (EP/M019918/1)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Machine Learning and Data Classification
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
- Physical Sciences › Computer Science › Artificial Intelligence › Domain Adaptation and Few-Shot Learning
- Keywords
- object detection, label noise, noisy training data, co-teaching, automatic labeling, vehicle detection, KITTI dataset
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
