Best Practices in Active Learning for Semantic Segmentation Article Swipe
Sudhanshu Mittal
,
Joshua Niemeijer
,
Jörg P. Schäfer
,
Thomas Brox
·
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2302.04075
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2302.04075
Active learning is particularly of interest for semantic segmentation, where annotations are costly. Previous academic studies focused on datasets that are already very diverse and where the model is trained in a supervised manner with a large annotation budget. In contrast, data collected in many driving scenarios is highly redundant, and most medical applications are subject to very constrained annotation budgets. This work investigates the various types of existing active learning methods for semantic segmentation under diverse conditions across three dimensions - data distribution w.r.t. different redundancy levels, integration of semi-supervised learning, and different labeling budgets. We find that these three underlying factors are decisive for the selection of the best active learning approach. As an outcome of our study, we provide a comprehensive usage guide to obtain the best performance for each case. We also propose an exemplary evaluation task for driving scenarios, where data has high redundancy, to showcase the practical implications of our research findings.
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Metadata
- Type
- preprint
- Language
- en
- Landing Page
- http://arxiv.org/abs/2302.04075
- https://arxiv.org/pdf/2302.04075
- OA Status
- green
- Cited By
- 3
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4319793456
All OpenAlex metadata
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- OpenAlex ID
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https://openalex.org/W4319793456Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.48550/arxiv.2302.04075Digital Object Identifier
- Title
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Best Practices in Active Learning for Semantic SegmentationWork title
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preprintOpenAlex work type
- Language
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enPrimary language
- Publication year
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2023Year of publication
- Publication date
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2023-02-08Full publication date if available
- Authors
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Sudhanshu Mittal, Joshua Niemeijer, Jörg P. Schäfer, Thomas BroxList of authors in order
- Landing page
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https://arxiv.org/abs/2302.04075Publisher landing page
- PDF URL
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https://arxiv.org/pdf/2302.04075Direct link to full text PDF
- Open access
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YesWhether a free full text is available
- OA status
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greenOpen access status per OpenAlex
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https://arxiv.org/pdf/2302.04075Direct OA link when available
- Concepts
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Computer science, Annotation, Segmentation, Redundancy (engineering), Machine learning, Artificial intelligence, Active learning (machine learning), Operating systemTop concepts (fields/topics) attached by OpenAlex
- Cited by
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3Total citation count in OpenAlex
- Citations by year (recent)
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2024: 2, 2023: 1Per-year citation counts (last 5 years)
- Related works (count)
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10Other works algorithmically related by OpenAlex
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