A Few Guidelines for Incremental Few-Shot Segmentation. Article Swipe
Fabio Cermelli
,
Massimiliano Mancini
,
Yongqin Xian
,
Zeynep Akata
,
Barbara Caputo
·
YOU?
·
· 2020
· Open Access
·
YOU?
·
· 2020
· Open Access
·
Reducing the amount of supervision required by neural networks is especially important in the context of semantic segmentation, where collecting dense pixel-level annotations is particularly expensive. In this paper, we address this problem from a new perspective: Incremental Few-Shot Segmentation. In particular, given a pretrained segmentation model and few images containing novel classes, our goal is to learn to segment novel classes while retaining the ability to segment previously seen ones. In this context, we discover, against all beliefs, that fine-tuning the whole architecture with these few images is not only meaningful, but also very effective. We show how the main problems of end-to-end training in this scenario are i) the drift of the batch-normalization statistics toward novel classes that we can fix with batch renormalization and ii) the forgetting of old classes, that we can fix with regularization strategies. We summarize our findings with five guidelines that together consistently lead to the state of the art on the COCO and Pascal-VOC 2012 datasets, with different number of images per class and even with multiple learning episodes.
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- Type
- preprint
- Language
- en
- Landing Page
- http://export.arxiv.org/pdf/2012.01415
- OA Status
- green
- Cited By
- 6
- References
- 32
- Related Works
- 20
- OpenAlex ID
- https://openalex.org/W3108603282
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- OpenAlex ID
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https://openalex.org/W3108603282Canonical identifier for this work in OpenAlex
- Title
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A Few Guidelines for Incremental Few-Shot Segmentation.Work title
- Type
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preprintOpenAlex work type
- Language
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enPrimary language
- Publication year
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2020Year of publication
- Publication date
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2020-11-30Full publication date if available
- Authors
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Fabio Cermelli, Massimiliano Mancini, Yongqin Xian, Zeynep Akata, Barbara CaputoList of authors in order
- Landing page
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https://export.arxiv.org/pdf/2012.01415Publisher landing page
- Open access
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YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
- OA URL
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https://export.arxiv.org/pdf/2012.01415Direct OA link when available
- Concepts
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Segmentation, Computer science, Pascal (unit), Artificial intelligence, Regularization (linguistics), Normalization (sociology), Machine learning, Forgetting, Context (archaeology), Pattern recognition (psychology), Geography, Anthropology, Linguistics, Philosophy, Archaeology, Sociology, Programming languageTop concepts (fields/topics) attached by OpenAlex
- Cited by
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6Total citation count in OpenAlex
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2023: 1, 2022: 1, 2021: 4Per-year citation counts (last 5 years)
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32Number of works referenced by this work
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20Other works algorithmically related by OpenAlex
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| abstract_inverted_index.summarize | 141 |
| abstract_inverted_index.Pascal-VOC | 161 |
| abstract_inverted_index.collecting | 19 |
| abstract_inverted_index.containing | 50 |
| abstract_inverted_index.effective. | 95 |
| abstract_inverted_index.end-to-end | 103 |
| abstract_inverted_index.especially | 10 |
| abstract_inverted_index.expensive. | 25 |
| abstract_inverted_index.forgetting | 129 |
| abstract_inverted_index.guidelines | 146 |
| abstract_inverted_index.pretrained | 44 |
| abstract_inverted_index.previously | 68 |
| abstract_inverted_index.statistics | 115 |
| abstract_inverted_index.Incremental | 37 |
| abstract_inverted_index.annotations | 22 |
| abstract_inverted_index.fine-tuning | 80 |
| abstract_inverted_index.meaningful, | 91 |
| abstract_inverted_index.particular, | 41 |
| abstract_inverted_index.pixel-level | 21 |
| abstract_inverted_index.strategies. | 139 |
| abstract_inverted_index.supervision | 4 |
| abstract_inverted_index.architecture | 83 |
| abstract_inverted_index.consistently | 149 |
| abstract_inverted_index.particularly | 24 |
| abstract_inverted_index.perspective: | 36 |
| abstract_inverted_index.segmentation | 45 |
| abstract_inverted_index.Segmentation. | 39 |
| abstract_inverted_index.segmentation, | 17 |
| abstract_inverted_index.regularization | 138 |
| abstract_inverted_index.renormalization | 125 |
| abstract_inverted_index.batch-normalization | 114 |
| cited_by_percentile_year | |
| countries_distinct_count | 0 |
| institutions_distinct_count | 5 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/4 |
| sustainable_development_goals[0].score | 0.5099999904632568 |
| sustainable_development_goals[0].display_name | Quality Education |
| citation_normalized_percentile |