Deep active learning for object detection
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Proposed methods do not always fully explore their potential by focusing on an exclusive criterion when considering several complementary sampling strategies simultaneously is possible.
“However, the proposed methods do not always fully explore their potential by focusing on an exclusive criteria-among the previously referenced ones-when considering several complementary sampling strategies simultaneously is possible [36,37,44,45,57,61,67].”
From Ten Years of Active Learning Techniques and Object Detection: A Systematic Review (2023) · cites this paper as [45]
Aggregation of information from multiple sources such as pixel-level, box-level, or image-level has been useful for both uncertainty-based and diversity-based methods.
“Multiple alternatives include the aggregation of information from multiple sources such as: pixel-level, box-level [45] or image-level [44,47], which have been useful for both uncertainty-based and diversity-based methods”
From Ten Years of Active Learning Techniques and Object Detection: A Systematic Review (2023) · cites this paper as [45]
Cites
Deep Residual Learning for Image Recognition
He, K., Zhang, X., Ren, S., & Sun, J. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.90 · Cited by 235,474 · see citing works
U-Net: Convolutional Networks for Biomedical Image Segmentation
Ronneberger, O., Fischer, P., & Brox, T. (2015) · Lecture notes in computer science · doi:10.1007/978-3-319-24574-4_28 · Cited by 99,404 · 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
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
FCOS: Fully Convolutional One-Stage Object Detection
Tian, Z., Shen, C., Chen, H., & He, T. (2019) · 2019 IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2019.00972 · Cited by 7,013 · see citing works
Focal Loss for Dense Object Detection
Lin, T.-Y., Goyal, P., Girshick, R., He, K., & Dollár, P. (2017) · arXiv · doi:10.48550/arxiv.1708.02002 · Cited by 1,227 · see citing works
A Survey of Deep Active Learning
Ren, P., Xiao, Y., Chang, X., Huang, P.-Y., Li, Z., Gupta, B. B., Chen, X., & Wang, X. (2022) · ACM Computing Surveys · doi:10.1145/3472291 · Cited by 1,169 · see citing works
The Power of Ensembles for Active Learning in Image Classification
Beluch, W. H., Genewein, T., Nurnberger, A., & Kohler, J. M. (2018) · 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2018.00976 · Cited by 655 · see citing works
Active Learning for Deep Detection Neural Networks
Aghdam, H. H., Gonzalez-Garcia, A., Lopez, A., & Weijer, J. (2019) · 2019 IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2019.00377 · Cited by 148 · see citing works
Active Learning for Deep Object Detection
Brust, C.-A., Käding, C., & Denzler, J. (2019) · Proceedings of the 14th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications · doi:10.5220/0007248601810190 · Cited by 78 · see citing works
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Ten Years of Active Learning Techniques and Object Detection: A Systematic Review
Garcia, D., Carias, J., Adão, T., Jesus, R., Cunha, A., & Magalhães, L. G. (2023) · Applied Sciences · doi:10.3390/app131910667 · Cited by 17 · see citing works
Not All Labels Are Equal: Rationalizing The Labeling Costs for Training Object Detection
Elezi, I., Yu, Z., Anandkumar, A., Leal-Taixe, L., & Alvarez, J. M. (2021) · arXiv · Cited by 50 · see citing works
- Authors
- Li, Y., Fan, B., Zhang, W., Ding, W., & Yin, J.
- Affiliations
- Zhejiang University (CN)
- Nantong University (CN)
- Corresponding author
- Fan, Binbin
- Venue
- Information Sciences
- Publisher
- Elsevier BV
- Published
- 2021-11
- Volume
- 579
- Pages
- 418-433
- ISSN
- 0020-0255 (ISSN-L)
- OpenAlex
- W3187627504
- Cited by
- 60
- References
- 10
- Citations by year
- 2026: 5 · 2025: 9 · 2024: 16 · 2023: 15 · 2022: 13 · 2021: 1 · 2019: 1
- FWCI
- 3.98
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- Locations
- Information Sciences (publishedVersion)
- Funders
- Natural Science Foundation of Jiangsu Province (BK20151274, BK20191445)
- Qinglan Project of Jiangsu Province of China
- National Natural Science Foundation of China (61300167, 61976120)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Machine Learning and Algorithms
- 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
- active learning (machine learning), object detection, deep active learning, sample selection, mean average precision, class imbalance, uncertainty sampling, batch mode active learning, annotation cost reduction
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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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