Localization-Aware Active Learning for Object Detection
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Active learning techniques focus on acquiring a large and diverse set of examples while reducing the time needed by researchers or professionals to screen data.
“A valid way to overcome these challenges lies in the application of ALtechniques [5-8], which focuses on the acquisition of a large and diverse set of examples, while reducing the time needed by researchers/professionals to screen the data.”
From Ten Years of Active Learning Techniques and Object Detection: A Systematic Review (2023) · cites this paper as [5]
Asia (China), North America (United States of America), and Europe (Germany) are producing the main advances in active learning for object detection research.
“An analysis of the research groups or technological companies working in AL for OD showed that Asia (China), North America (United States of America), and Europe (Germany) are producing the main advances in this research field (e.g., [5,36,55]), as can be seen in Figure 5.”
From Ten Years of Active Learning Techniques and Object Detection: A Systematic Review (2023) · cites this paper as [5]
The random sampling strategy is mostly used for comparison purposes, based on the principle that AL-based methods have the potential to perform better.
“The random sampling strategy is mostly used for comparison purposes, grounded on to the principle that AL-based methods have the potential to perform better (e.g., [5,48,52,60]).”
From Ten Years of Active Learning Techniques and Object Detection: A Systematic Review (2023) · cites this paper as [5]
A good instance selection method for object detection must consider the convolutional neural network's ability to classify and locate, even when it is challenging to determine object-location informativeness.
“Therefore, a good instance selection method for object detection must consider the convolutional neural network's ability to classify and locate, even in conditions wherein it is challenging to determine object-location informativeness [5].”
From Ten Years of Active Learning Techniques and Object Detection: A Systematic Review (2023) · cites this paper as [5]
Cites
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
Instance-aware Semantic Segmentation via Multi-task Network Cascades
Dai, J., He, K., & Sun, J. (2015) · arXiv · doi:10.48550/arxiv.1512.04412 · Cited by 1,273 · see citing works
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Active Learning for Deep Object Detection via Probabilistic Modeling
Choi, J., Elezi, I., Lee, H.-J., Farabet, C., & Alvarez, J. M. (2021) · 2021 IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv48922.2021.01010 · Cited by 135 · see citing works
Deep Active Learning for Computer Vision: Past and Future
Takezoe, R., Liu, X., Mao, S., Chen, M. T., Feng, Z., Zhang, S., & Wang, X. (2023) · APSIPA Transactions on Signal and Information Processing · doi:10.1561/116.00000057 · Cited by 26 · see citing works
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
QBox: Partial Transfer Learning With Active Querying for Object Detection
Tang, Y.-P., Wei, X.-S., Zhao, B., & Huang, S.-J. (2023) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2021.3111621 · Cited by 17 · see citing works
Deep active learning with Weighting filter for object detection
Huang, W., Sun, S., Lin, X., Zhang, D., & Ma, L. (2023) · Displays · doi:10.1016/j.displa.2022.102282 · Cited by 11 · 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
- Kao, C.-C., Lee, T.-Y., Sen, P., & Liu, M.-Y.
- Affiliations
- University of California, Santa Barbara (US)
- Mitsubishi Electric Research Laboratories (United States) (US)
- Corresponding author
- Lee, Teng-Yok
- Venue
- Lecture Notes in Computer Science
- Publisher
- Springer International Publishing
- Published
- 2019
- Pages
- 506-522
- ISSN
- 0302-9743 (ISSN-L)
- 1611-3349
- OpenAlex
- W2783822844
- Cited by
- 118
- References
- 2
- Citations by year
- 2026: 5 · 2025: 14 · 2024: 19 · 2023: 27 · 2022: 17 · 2021: 18 · 2020: 12 · 2019: 4 · 2018: 2
- FWCI
- 4.88
- Type
- conference paper
- Language
- en
- Open access
- green
- arxiv.org
- Licence
- www.springer.com/tdm (tdm)
- Locations
- Lecture notes in computer science (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University)
- 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, region proposal, 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.
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