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Cited by 6Machine Learning and AlgorithmsDomain Adaptation and Few-Shot LearningMachine Learning and Data Classification

TBAL: Two-stage batch-mode active learning for image classification

Shen, Yeji, Song, Yuhang, Wu, Chi-hao, Kuo, C.-C. Jay·2022·Signal Processing: Image Communication
DOI 10.1016/j.image.2022.116731
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Crossref · 10.1016/j.image.2022.116731

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1 of 1 references verified0 citation errors · 0 not checkable

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  • Active learning has been successful in applications involving image classification, segmentation, pose estimation, and visual tracking.

    “Active learning has been successful in applications involving image classification [39,40], segmentation [41], pose estimation [7], and visual tracking [42] while its use in other tasks, such as OD, needs more attention [36].”

    From Ten Years of Active Learning Techniques and Object Detection: A Systematic Review (2023) · cites this paper as [40]

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Authors
Shen, Y., Song, Y., Wu, C.-H., & Kuo, C.-C. J.
Affiliations
University of Southern California (US)
Corresponding author
Shen, Yeji
ORCID
Venue
Signal Processing: Image Communication
Publisher
Elsevier BV
Published
2022-08
Volume
106
Pages
116731
ISSN
0923-5965 (ISSN-L)
DOI
10.1016/j.image.2022.116731
OpenAlex
W4280634096
Cited by
6
References
1
Citations by year
2026: 1 · 2024: 3 · 2023: 1 · 2022: 1
FWCI
0.41
Type
journal article
Language
en
Open access
closed
Licence
Locations
Signal Processing Image Communication (publishedVersion)
Topics
  • Physical Sciences › Computer Science › Artificial Intelligence › Machine Learning and Algorithms
  • Physical Sciences › Computer Science › Artificial Intelligence › Domain Adaptation and Few-Shot Learning
  • Physical Sciences › Computer Science › Artificial Intelligence › Machine Learning and Data Classification
Keywords
active learning (machine learning), batch mode active learning, semi-supervised learning, uncertainty estimation, query strategies, transfer learning, image classification, unlabeled data

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