Acquisition of Localization Confidence for Accurate Object Detection
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The most intuitive improvement is to directly use IoU as the localization loss.
“The most intuitive improvement is to directly use IoU as the localization loss [105-107, 190].”
From Object Detection in 20 Years: A Survey (2023) · cites this paper as [190]
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
Microsoft COCO: Common Objects in Context
Lin, T.-Y., Maire, M., Belongie, S., Hays, J., Perona, P., Ramanan, D., Dollár, P., & Zitnick, C. L. (2014) · Lecture Notes in Computer Science · doi:10.1007/978-3-319-10602-1_48 · Cited by 45,460 · 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
Histograms of Oriented Gradients for Human Detection
Dalal, N., & Triggs, B. (2005) · 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) · doi:10.1109/cvpr.2005.177 · Cited by 33,584 · see citing works
Rich Feature Hierarchies for Accurate Object Detection and Semantic Segmentation
Girshick, R., Donahue, J., Darrell, T., & Malik, J. (2014) · 2014 IEEE Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2014.81 · Cited by 32,684 · see citing works
Feature Pyramid Networks for Object Detection
Lin, T.-Y., Dollar, P., Girshick, R., He, K., Hariharan, B., & Belongie, S. (2017) · 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2017.106 · Cited by 30,939 · see citing works
He, K., Gkioxari, G., Dollar, P., & Girshick, R. (2017) · 2017 IEEE International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2017.322 · Cited by 29,137 · see citing works
Girshick, R. (2015) · 2015 IEEE International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2015.169 · Cited by 28,105 · see citing works
Advances in neural information processing systems 7
Cios, K. J., & Shields, M. E. (1997) · Neurocomputing · doi:10.1016/s0925-2312(97)90018-7 · Cited by 22,089 · see citing works
Cascade R-CNN: Delving Into High Quality Object Detection
Cai, Z., & Vasconcelos, N. (2018) · 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2018.00644 · Cited by 7,370 · see citing works
DeepFace: Closing the Gap to Human-Level Performance in Face Verification
Taigman, Y., Yang, M., Ranzato, M., & Wolf, L. (2014) · 2014 IEEE Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2014.220 · Cited by 6,542 · see citing works
Selective Search for Object Recognition
Uijlings, J. R. R., van de Sande, K. E. A., Gevers, T., & Smeulders, A. W. M. (2013) · International Journal of Computer Vision · doi:10.1007/s11263-013-0620-5 · Cited by 5,980 · see citing works
DeepPose: Human Pose Estimation via Deep Neural Networks
Toshev, A., & Szegedy, C. (2014) · 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.2014.214 · Cited by 3,288 · see citing works
Edge Boxes: Locating Object Proposals from Edges
Zitnick, C. L., & Dollár, P. (2014) · Lecture Notes in Computer Science · doi:10.1007/978-3-319-10602-1_26 · Cited by 2,656 · see citing works
Soft-NMS — Improving Object Detection with One Line of Code
Bodla, N., Singh, B., Chellappa, R., & Davis, L. S. (2017) · 2017 IEEE International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2017.593 · Cited by 2,274 · cited 4× · see citing works
“Quantitative results show that our method outperforms traditional NMS and other variants such as Soft-NMS [2].”
BackgroundMethodInfluentialUnitBox: An Advanced Object Detection Network
Yu, J., Jiang, Y., Wang, Z., Cao, Z., & Huang, T. (2016) · Proceedings of the 24th ACM international conference on Multimedia · doi:10.1145/2964284.2967274 · Cited by 1,731 · see citing works
Self-citationRelation Networks for Object Detection
Hu, H., Gu, J., Zhang, Z., Dai, J., & Wei, Y. (2018) · 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2018.00378 · Cited by 1,524 · see citing works
Inside-Outside Net: Detecting Objects in Context with Skip Pooling and Recurrent Neural Networks
Bell, S., Zitnick, C. L., Bala, K., & Girshick, R. (2016) · 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.2016.314 · Cited by 1,344 · see citing works
Object Detection via a Multi-region and Semantic Segmentation-Aware CNN Model
Gidaris, S., & Komodakis, N. (2015) · 2015 IEEE International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2015.135 · Cited by 801 · see citing works
Learning to Refine Object Segments
Pinheiro, P. O., Lin, T.-Y., Collobert, R., & Dollár, P. (2016) · Lecture notes in computer science · doi:10.1007/978-3-319-46448-0_5 · Cited by 768 · see citing works
Learning Non-maximum Suppression
Hosang, J., Benenson, R., & Schiele, B. (2017) · 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2017.685 · Cited by 736 · see citing works
Learning to Segment Object CandidatesPreprint
Pinheiro, P. H. O., Collobert, R., & Dollár, P. (2015) · arXiv (Cornell University) · doi:10.48550/arxiv.1506.06204 · Cited by 710 · see citing works
Repulsion Loss: Detecting Pedestrians in a Crowd
Wang, X., Xiao, T., Jiang, Y., Shao, S., Sun, J., & Shen, C. (2018) · 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.2018.00811 · Cited by 620 · see citing works
Self-citationDenseBox: Unifying Landmark Localization with End to End Object Detection
Huang, L., Yang, Y., Deng, Y., & Yu, Y. (2015) · arXiv · doi:10.48550/arxiv.1509.04874 · Cited by 407 · see citing works
Non-maximum Suppression for Object Detection by Passing Messages Between Windows
Rothe, R., Guillaumin, M., & Van Gool, L. (2015) · Lecture Notes in Computer Science · doi:10.1007/978-3-319-16865-4_19 · Cited by 218 · see citing works
G-CNN: An Iterative Grid Based Object Detector
Najibi, M., Rastegari, M., & Davis, L. S. (2016) · 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.2016.260 · Cited by 203 · see citing works
Yang, B., Yan, J., Lei, Z., & Li, S. Z. (2016) · 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.2016.650 · Cited by 131 · see citing works
Learning to See Physics via Visual De-animation
Wu, J., Lu, E., Kohli, P., Freeman, B., & Tenenbaum, J. B. (2017) · DSpace@MIT (Massachusetts Institute of Technology) · Cited by 113 · see citing works
Attend Refine Repeat: Active Box Proposal Generation via In-Out Localization
Komodakis, N., & Gidaris, S. (2016) · HAL (Le Centre pour la Communication Scientifique Directe) · Cited by 79 · see citing works
Multistage Object Detection With Group Recursive Learning
Li, J., Liang, X., Li, J., Wei, Y., Xu, T., Feng, J., & Yan, S. (2017) · IEEE Transactions on Multimedia · doi:10.1109/tmm.2017.2772796 · Cited by 59 · see citing works
FastMask: Segment Multi-scale Object Candidates in One Shot
Hu, H., Lan, S., Jiang, Y., Cao, Z., & Sha, F. (2017) · 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.2017.245 · Cited by 32 · see citing works
Self-citationRefineNet: Iterative refinement for accurate object localization
Rajaram, R. N., Ohn-Bar, E., & Trivedi, M. M. (2016) · 2016 IEEE 19th International Conference on Intelligent Transportation Systems (ITSC) · doi:10.1109/itsc.2016.7795760 · Cited by 28 · see citing works
Multi-stage Object Detection with Group Recursive LearningPreprint
Li, J., Liang, X., Li, J., et al. (2016) · arXiv (Cornell University) · doi:10.48550/arxiv.1608.05159 · Cited by 6 · see citing works
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Rezatofighi, H., Tsoi, N., Gwak, J., Sadeghian, A., Reid, I., & Savarese, S. (2019) · 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2019.00075 · Cited by 6,612 · see citing works
Object Detection in 20 Years: A Survey
Zou, Z., Chen, K., Shi, Z., Guo, Y., & Ye, J. (2023) · Proceedings of the IEEE · doi:10.1109/jproc.2023.3238524 · Cited by 3,689 · see citing works
Deep Learning for Generic Object Detection: A Survey
Liu, L., Ouyang, W., Wang, X., Fieguth, P., Chen, J., Liu, X., & Pietikäinen, M. (2020) · International Journal of Computer Vision · doi:10.1007/s11263-019-01247-4 · Cited by 2,965 · see citing works
Semantic Understanding of Scenes Through the ADE20K Dataset
Zhou, B., Zhao, H., Puig, X., Xiao, T., Fidler, S., Barriuso, A., & Torralba, A. (2019) · International Journal of Computer Vision · doi:10.1007/s11263-018-1140-0 · Cited by 1,831 · see citing works
Self-citationTOOD: Task-aligned One-stage Object Detection
Feng, C., Zhong, Y., Gao, Y., Scott, M. R., & Huang, W. (2021) · 2021 IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv48922.2021.00349 · Cited by 1,668 · see citing works
“It is commonly formulated as a multi-task learning problem by jointly optimizing object classification and localization [4, 6, 7, 16, 22, 32].”
BackgroundHybrid Task Cascade for Instance Segmentation
Chen, K., Pang, J., Wang, J., Xiong, Y., Li, X., Sun, S., Feng, W., Liu, Z., Shi, J., Ouyang, W., et al. (2019) · 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2019.00511 · Cited by 1,587 · see citing works
Zheng, Z., Wang, P., Ren, D., Liu, W., Ye, R., Hu, Q., & Zuo, W. (2022) · IEEE Transactions on Cybernetics · doi:10.1109/tcyb.2021.3095305 · Cited by 1,552 · see citing works
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Zhou, X., Zhuo, J., & Krahenbuhl, P. (2019) · 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2019.00094 · Cited by 1,088 · see citing works
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Deng, J., Shi, S., Li, P., Zhou, W., Zhang, Y., & Li, H. (2021) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v35i2.16207 · Cited by 1,048 · see citing works
Distance-IoU Loss: Faster and Better Learning for Bounding Box Regression
Zheng, Z., Wang, P., Liu, W., Li, J., Ye, R., & Ren, D. (2019) · arXiv · doi:10.48550/arxiv.1911.08287 · Cited by 5,313 · see citing works
SiamFC++: Towards Robust and Accurate Visual Tracking with Target Estimation Guidelines
Xu, Y., Wang, Z., Li, Z., Yuan, Y., & Yu, G. (2020) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v34i07.6944 · Cited by 974 · see citing works
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Danelljan, M., Van Gool, L., & Timofte, R. (2020) · 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr42600.2020.00721 · Cited by 798 · see citing works
Bounding Box Regression With Uncertainty for Accurate Object Detection
He, Y., Zhu, C., Wang, J., Savvides, M., & Zhang, X. (2019) · 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2019.00300 · Cited by 623 · see citing works
A review of object detection based on deep learning
Xiao, Y., Tian, Z., Yu, J., Zhang, Y., Liu, S., Du, S., & Lan, X. (2020) · Multimedia Tools and Applications · doi:10.1007/s11042-020-08976-6 · Cited by 584 · see citing works
Adaptive NMS: Refining Pedestrian Detection in a Crowd
Liu, S., Huang, D., & Wang, Y. (2019) · 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2019.00662 · Cited by 413 · see citing works
Deep Learning for Generic Object Detection: A Survey
Liu, L., Ouyang, W., Wang, X., Fieguth, P., Chen, J., Liu, X., & Pietikäinen, M. (2018) · arXiv · doi:10.48550/arxiv.1809.02165 · Cited by 2,965 · see citing works
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Wang, G., Luo, C., Sun, X., Xiong, Z., & Zeng, W. (2020) · 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr42600.2020.00632 · Cited by 206 · see citing works
LUVLi Face Alignment: Estimating Landmarks’ Location, Uncertainty, and Visibility Likelihood
Kumar, A., Marks, T. K., Mou, W., Wang, Y., Jones, M., Cherian, A., Koike-Akino, T., Liu, X., & Feng, C. (2020) · 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr42600.2020.00826 · Cited by 165 · see citing works
3D Object Detection from Images for Autonomous Driving: A Survey
Ma, X., Ouyang, W., Simonelli, A., & Ricci, E. (2022) · arXiv · doi:10.48550/arxiv.2202.02980 · Cited by 147 · see citing works
Learning to Rank Proposals for Object Detection
Tan, Z., Nie, X., Qian, Q., Li, N., & Li, H. (2019) · 2019 IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2019.00836 · Cited by 56 · see citing works
From Algorithm to Hardware: A Survey on Efficient and Safe Deployment of Deep Neural Networks
Geng, X., Wang, Z., Chen, C., Xu, Q., Xu, K., Jin, C., Gupta, M., Yang, X., Chen, Z., Sabry Aly, M. M., et al. (2025) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2024.3394494 · Cited by 26 · see citing works
A Review on Object Detection Based on Deep Convolutional Neural Networks for Autonomous Driving
Lu, J., Tang, S., Wang, J., Zhu, H., & Wang, Y. (2019) · 2019 Chinese Control And Decision Conference (CCDC) · doi:10.1109/ccdc.2019.8832398 · Cited by 17 · see citing works
- Authors
- Jiang, B., Luo, R., Mao, J., Xiao, T., & Jiang, Y.
- Affiliations
- Peking University (CN)
- Tsinghua University (CN)
- Corresponding author
- Mao, Jiayuan
- Venue
- Lecture Notes in Computer Science
- Publisher
- Springer International Publishing
- Published
- 2018
- Pages
- 816-832
- ISSN
- 0302-9743 (ISSN-L)
- 1611-3349
- OpenAlex
- W2886904239
- Cited by
- 1,059
- References
- 33
- Citations by year
- 2026: 39 · 2025: 90 · 2024: 146 · 2023: 187 · 2022: 193 · 2021: 181 · 2020: 153 · 2019: 62 · 2018: 8
- FWCI
- 30.44
- Type
- conference paper
- Language
- en
- Open access
- green
- arxiv.org
- Locations
- Lecture notes in computer science (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Image and Video Retrieval Techniques
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Visual Attention and Saliency Detection
- Keywords
- object detection, bounding box regression, non-maximum suppression, intersection over union, bounding box refinement
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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.
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