Improving the Intra-class Long-Tail in 3D Detection via Rare Example Mining
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A significant part of the works found in the literature addresses object detection over 2D data, while 3D data are also focused on by other approaches.
“Even though a significant part of the works found in the literature addresses OD over 2D data (see Table 2), 3D data are also focused on by other approaches (e.g., [15,53]), in which the application of AL techniques proved to be beneficial.”
From Ten Years of Active Learning Techniques and Object Detection: A Systematic Review (2023) · cites this paper as [15]
Clustering algorithms have been widely applied to optimise criteria such as diversity, representativeness, rarity, and density.
“Clustering algorithms, for example, have been widely applied to optimise criteria such as diversity [49], representativeness [67], rarity [15,50], and density [16,78].”
From Ten Years of Active Learning Techniques and Object Detection: A Systematic Review (2023) · cites this paper as [15]
Cites
nuScenes: A Multimodal Dataset for Autonomous Driving
Caesar, H., Bankiti, V., Lang, A. H., Vora, S., Liong, V. E., Xu, Q., Krishnan, A., Pan, Y., Baldan, G., & Beijbom, O. (2020) · 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr42600.2020.01164 · Cited by 7,152 · see citing works
LVIS: A Dataset for Large Vocabulary Instance Segmentation
Gupta, A., Dollar, P., & Girshick, R. (2019) · 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2019.00550 · Cited by 1,274 · 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 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
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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
- Authors
- Jiang, C. M., Najibi, M., Qi, C. R., Zhou, Y., & Anguelov, D.
- Corresponding author
- Jiang, Chiyu Max
- ORCID
- Zhou, Yin
- Venue
- Lecture Notes in Computer Science
- Publisher
- Springer Nature Switzerland
- Published
- 2022
- Pages
- 158-175
- ISSN
- 0302-9743 (ISSN-L)
- 1611-3349
- OpenAlex
- W4313179489
- Cited by
- 21
- References
- 4
- Citations by year
- 2026: 4 · 2025: 4 · 2024: 8 · 2023: 5
- FWCI
- 1.54
- 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 › Artificial Intelligence › Domain Adaptation and Few-Shot Learning
- Physical Sciences › Engineering › Automotive Engineering › Autonomous Vehicle Technology and Safety
- Keywords
- 3D object detection, density estimation, flow model, data-centric learning, class imbalance
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.
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