Learning to Detect Semantic Boundaries with Image-level Class Labels Article Swipe
Namyup Kim
,
Sehyun Hwang
,
Suha Kwak
·
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2212.07579
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2212.07579
This paper presents the first attempt to learn semantic boundary detection using image-level class labels as supervision. Our method starts by estimating coarse areas of object classes through attentions drawn by an image classification network. Since boundaries will locate somewhere between such areas of different classes, our task is formulated as a multiple instance learning (MIL) problem, where pixels on a line segment connecting areas of two different classes are regarded as a bag of boundary candidates. Moreover, we design a new neural network architecture that can learn to estimate semantic boundaries reliably even with uncertain supervision given by the MIL strategy. Our network is used to generate pseudo semantic boundary labels of training images, which are in turn used to train fully supervised models. The final model trained with our pseudo labels achieves an outstanding performance on the SBD dataset, where it is as competitive as some of previous arts trained with stronger supervision.
Related Topics
Concepts
Computer science
Boundary (topology)
Artificial intelligence
Class (philosophy)
Task (project management)
Image (mathematics)
Object (grammar)
Artificial neural network
Pixel
Pattern recognition (psychology)
Contextual image classification
Machine learning
Natural language processing
Mathematics
Engineering
Systems engineering
Mathematical analysis
Metadata
- Type
- preprint
- Language
- en
- Landing Page
- http://arxiv.org/abs/2212.07579
- https://arxiv.org/pdf/2212.07579
- OA Status
- green
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4311725733
All OpenAlex metadata
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- OpenAlex ID
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https://openalex.org/W4311725733Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.48550/arxiv.2212.07579Digital Object Identifier
- Title
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Learning to Detect Semantic Boundaries with Image-level Class LabelsWork title
- Type
-
preprintOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2022Year of publication
- Publication date
-
2022-12-15Full publication date if available
- Authors
-
Namyup Kim, Sehyun Hwang, Suha KwakList of authors in order
- Landing page
-
https://arxiv.org/abs/2212.07579Publisher landing page
- PDF URL
-
https://arxiv.org/pdf/2212.07579Direct link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
- OA URL
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https://arxiv.org/pdf/2212.07579Direct OA link when available
- Concepts
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Computer science, Boundary (topology), Artificial intelligence, Class (philosophy), Task (project management), Image (mathematics), Object (grammar), Artificial neural network, Pixel, Pattern recognition (psychology), Contextual image classification, Machine learning, Natural language processing, Mathematics, Engineering, Systems engineering, Mathematical analysisTop concepts (fields/topics) attached by OpenAlex
- Cited by
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0Total citation count in OpenAlex
- Related works (count)
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.competitive | 145 |
| abstract_inverted_index.image-level | 12 |
| abstract_inverted_index.outstanding | 135 |
| abstract_inverted_index.performance | 136 |
| abstract_inverted_index.supervision | 96 |
| abstract_inverted_index.architecture | 84 |
| abstract_inverted_index.supervision. | 16, 154 |
| abstract_inverted_index.classification | 33 |
| cited_by_percentile_year | |
| countries_distinct_count | 0 |
| institutions_distinct_count | 3 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/4 |
| sustainable_development_goals[0].score | 0.699999988079071 |
| sustainable_development_goals[0].display_name | Quality Education |
| citation_normalized_percentile |