The DOI matches its Crossref record; 36 of the 36 references it cites were found in a registry or an index.
Adaptive Active Learning for Image Classification
Abstract
Recently active learning has attracted a lot of attention in computer vision field, as it is time and cost consuming to prepare a good set of labeled images for vision data analysis. Most existing active learning approaches employed in computer vision adopt most uncertainty measures as instance selection criteria. Although most uncertainty query selection strategies are very effective in many circumstances, they fail to take information in the large amount of unlabeled instances into account and are prone to querying outliers. In this paper, we present a novel adaptive active learning approach that combines an information density measure and a most uncertainty measure together to select critical instances to label for image classifications. Our experiments on two essential tasks of computer vision, object recognition and scene recognition, demonstrate the efficacy of the proposed approach.
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Cites
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Cited by
Active Learning for Convolutional Neural Networks: A Core-Set Approach
Sener, O., & Savarese, S. (2017) · arXiv · doi:10.48550/arxiv.1708.00489 · Cited by 922
Deep Bayesian Active Learning with Image Data
Gal, Y., Islam, R., & Ghahramani, Z. (2017) · arXiv · doi:10.48550/arxiv.1703.02910 · Cited by 900
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
Contextual Diversity for Active Learning
Agarwal, S., Arora, H., Anand, S., & Arora, C. (2020) · Lecture Notes in Computer Science · doi:10.1007/978-3-030-58517-4_9 · Cited by 176
Query-by-committee improvement with diversity and density in batch active learning
Kee, S., del Castillo, E., & Runger, G. (2018) · Information Sciences · doi:10.1016/j.ins.2018.05.014 · Cited by 98
Metadata
- Authors
- Li, X., & Guo, Y.
- Venue
- 2013 IEEE Conference on Computer Vision and Pattern Recognition
- Publisher
- IEEE
- Published
- 2013-06
- Pages
- 859-866
- ISSN
- 1063-6919 (ISSN-L)
- OpenAlex
- W1975672287
- Cited by
- 268
- References
- 36
- FWCI
- 7.37
- Type
- conference paper
- Language
- en
- Open access
- closed
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