Remote Sensing Vision-Language Foundation Models without Annotations via Ground Remote Alignment Article Swipe
Utkarsh Mall
,
Cheng Perng Phoo
,
Meilin Kelsey Liu
,
Carl Vondrick
,
Bharath Hariharan
,
Kavita Bala
·
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2312.06960
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2312.06960
We introduce a method to train vision-language models for remote-sensing images without using any textual annotations. Our key insight is to use co-located internet imagery taken on the ground as an intermediary for connecting remote-sensing images and language. Specifically, we train an image encoder for remote sensing images to align with the image encoder of CLIP using a large amount of paired internet and satellite images. Our unsupervised approach enables the training of a first-of-its-kind large-scale vision language model (VLM) for remote sensing images at two different resolutions. We show that these VLMs enable zero-shot, open-vocabulary image classification, retrieval, segmentation and visual question answering for satellite images. On each of these tasks, our VLM trained without textual annotations outperforms existing VLMs trained with supervision, with gains of up to 20% for classification and 80% for segmentation.
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Metadata
- Type
- preprint
- Language
- en
- Landing Page
- http://arxiv.org/abs/2312.06960
- https://arxiv.org/pdf/2312.06960
- OA Status
- green
- Cited By
- 8
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4389714054
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4389714054Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.48550/arxiv.2312.06960Digital Object Identifier
- Title
-
Remote Sensing Vision-Language Foundation Models without Annotations via Ground Remote AlignmentWork title
- Type
-
preprintOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2023Year of publication
- Publication date
-
2023-12-12Full publication date if available
- Authors
-
Utkarsh Mall, Cheng Perng Phoo, Meilin Kelsey Liu, Carl Vondrick, Bharath Hariharan, Kavita BalaList of authors in order
- Landing page
-
https://arxiv.org/abs/2312.06960Publisher landing page
- PDF URL
-
https://arxiv.org/pdf/2312.06960Direct link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
- OA URL
-
https://arxiv.org/pdf/2312.06960Direct OA link when available
- Concepts
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Computer science, Artificial intelligence, Segmentation, Encoder, Computer vision, Key (lock), Vocabulary, The Internet, Remote sensing, Ground truth, Language model, Image (mathematics), World Wide Web, Philosophy, Operating system, Computer security, Linguistics, GeologyTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
8Total citation count in OpenAlex
- Citations by year (recent)
-
2025: 6, 2024: 2Per-year citation counts (last 5 years)
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.connecting | 33 |
| abstract_inverted_index.retrieval, | 98 |
| abstract_inverted_index.zero-shot, | 94 |
| abstract_inverted_index.annotations | 117 |
| abstract_inverted_index.large-scale | 75 |
| abstract_inverted_index.outperforms | 118 |
| abstract_inverted_index.annotations. | 15 |
| abstract_inverted_index.intermediary | 31 |
| abstract_inverted_index.resolutions. | 87 |
| abstract_inverted_index.segmentation | 99 |
| abstract_inverted_index.supervision, | 123 |
| abstract_inverted_index.unsupervised | 67 |
| abstract_inverted_index.Specifically, | 38 |
| abstract_inverted_index.segmentation. | 135 |
| abstract_inverted_index.classification | 131 |
| abstract_inverted_index.remote-sensing | 9, 34 |
| abstract_inverted_index.classification, | 97 |
| abstract_inverted_index.open-vocabulary | 95 |
| abstract_inverted_index.vision-language | 6 |
| abstract_inverted_index.first-of-its-kind | 74 |
| cited_by_percentile_year | |
| countries_distinct_count | 0 |
| institutions_distinct_count | 6 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/4 |
| sustainable_development_goals[0].score | 0.8199999928474426 |
| sustainable_development_goals[0].display_name | Quality Education |
| citation_normalized_percentile |