GeoNet: Benchmarking Unsupervised Adaptation across Geographies Article Swipe
Tarun Kalluri
,
Wang‐Dong Xu
,
Manmohan Chandraker
·
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2303.15443
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2303.15443
In recent years, several efforts have been aimed at improving the robustness of vision models to domains and environments unseen during training. An important practical problem pertains to models deployed in a new geography that is under-represented in the training dataset, posing a direct challenge to fair and inclusive computer vision. In this paper, we study the problem of geographic robustness and make three main contributions. First, we introduce a large-scale dataset GeoNet for geographic adaptation containing benchmarks across diverse tasks like scene recognition (GeoPlaces), image classification (GeoImNet) and universal adaptation (GeoUniDA). Second, we investigate the nature of distribution shifts typical to the problem of geographic adaptation and hypothesize that the major source of domain shifts arise from significant variations in scene context (context shift), object design (design shift) and label distribution (prior shift) across geographies. Third, we conduct an extensive evaluation of several state-of-the-art unsupervised domain adaptation algorithms and architectures on GeoNet, showing that they do not suffice for geographical adaptation, and that large-scale pre-training using large vision models also does not lead to geographic robustness. Our dataset is publicly available at https://tarun005.github.io/GeoNet.
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- Type
- preprint
- Language
- en
- Landing Page
- http://arxiv.org/abs/2303.15443
- https://arxiv.org/pdf/2303.15443
- OA Status
- green
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4361194623
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https://openalex.org/W4361194623Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.48550/arxiv.2303.15443Digital Object Identifier
- Title
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GeoNet: Benchmarking Unsupervised Adaptation across GeographiesWork title
- Type
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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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2023-03-27Full publication date if available
- Authors
-
Tarun Kalluri, Wang‐Dong Xu, Manmohan ChandrakerList of authors in order
- Landing page
-
https://arxiv.org/abs/2303.15443Publisher landing page
- PDF URL
-
https://arxiv.org/pdf/2303.15443Direct link to full text PDF
- 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://arxiv.org/pdf/2303.15443Direct OA link when available
- Concepts
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Robustness (evolution), Computer science, Benchmarking, Adaptation (eye), Domain adaptation, Artificial intelligence, Spatial contextual awareness, Machine learning, Optics, Chemistry, Biochemistry, Classifier (UML), Business, Marketing, Gene, PhysicsTop concepts (fields/topics) attached by OpenAlex
- Cited by
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0Total citation count in OpenAlex
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10Other works algorithmically related by OpenAlex
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