Revisiting Weakly Supervised Pre-Training of Visual Perception Models Article Swipe
Mannat Singh
,
Laura Gustafson
,
Aaron Adcock
,
Vinicius de Freitas Reis
,
Buğra Gedik
,
Raj Prateek Kosaraju
,
Dhruv Mahajan
,
Ross Girshick
,
Piotr Dollár
,
Laurens van der Maaten
·
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2201.08371
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2201.08371
Model pre-training is a cornerstone of modern visual recognition systems. Although fully supervised pre-training on datasets like ImageNet is still the de-facto standard, recent studies suggest that large-scale weakly supervised pre-training can outperform fully supervised approaches. This paper revisits weakly-supervised pre-training of models using hashtag supervision with modern versions of residual networks and the largest-ever dataset of images and corresponding hashtags. We study the performance of the resulting models in various transfer-learning settings including zero-shot transfer. We also compare our models with those obtained via large-scale self-supervised learning. We find our weakly-supervised models to be very competitive across all settings, and find they substantially outperform their self-supervised counterparts. We also include an investigation into whether our models learned potentially troubling associations or stereotypes. Overall, our results provide a compelling argument for the use of weakly supervised learning in the development of visual recognition systems. Our models, Supervised Weakly through hashtAGs (SWAG), are available publicly.
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- Type
- preprint
- Language
- en
- Landing Page
- http://arxiv.org/abs/2201.08371
- https://arxiv.org/pdf/2201.08371
- OA Status
- green
- Cited By
- 5
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4221160932
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https://openalex.org/W4221160932Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.48550/arxiv.2201.08371Digital Object Identifier
- Title
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Revisiting Weakly Supervised Pre-Training of Visual Perception ModelsWork title
- Type
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preprintOpenAlex work type
- Language
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enPrimary language
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2022Year of publication
- Publication date
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2022-01-20Full publication date if available
- Authors
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Mannat Singh, Laura Gustafson, Aaron Adcock, Vinicius de Freitas Reis, Buğra Gedik, Raj Prateek Kosaraju, Dhruv Mahajan, Ross Girshick, Piotr Dollár, Laurens van der MaatenList of authors in order
- Landing page
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https://arxiv.org/abs/2201.08371Publisher landing page
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https://arxiv.org/pdf/2201.08371Direct link to full text PDF
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YesWhether a free full text is available
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greenOpen access status per OpenAlex
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https://arxiv.org/pdf/2201.08371Direct OA link when available
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Computer science, Machine learning, Artificial intelligence, Supervised learning, De facto, Transfer of learning, Scale (ratio), Artificial neural network, Political science, Quantum mechanics, Law, PhysicsTop concepts (fields/topics) attached by OpenAlex
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5Total citation count in OpenAlex
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2025: 1, 2024: 2, 2023: 2Per-year citation counts (last 5 years)
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.recognition | 8, 142 |
| abstract_inverted_index.supervision | 45 |
| abstract_inverted_index.associations | 120 |
| abstract_inverted_index.largest-ever | 54 |
| abstract_inverted_index.pre-training | 1, 13, 30, 40 |
| abstract_inverted_index.stereotypes. | 122 |
| abstract_inverted_index.corresponding | 59 |
| abstract_inverted_index.counterparts. | 107 |
| abstract_inverted_index.investigation | 112 |
| abstract_inverted_index.substantially | 103 |
| abstract_inverted_index.self-supervised | 86, 106 |
| abstract_inverted_index.transfer-learning | 71 |
| abstract_inverted_index.weakly-supervised | 39, 91 |
| cited_by_percentile_year | |
| countries_distinct_count | 0 |
| institutions_distinct_count | 10 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/4 |
| sustainable_development_goals[0].score | 0.5400000214576721 |
| sustainable_development_goals[0].display_name | Quality Education |
| citation_normalized_percentile |