Decoupling Common and Unique Representations for Multimodal Self-supervised Learning
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For segmentation tasks, Prithvi-EO-2.0-600M-TL and Prithvi-EO-2.0-600M show the highest performance, with a noticeable margin over the next best models, DeCUR and DINO ResNets.
“For segmentation, Prithvi-EO-2.0-600M-TL and Prithvi-EO-2.0-600M show the highest performance, with a noticeable margin over the next best models, DeCUR [7] and DINO [12] ResNets.”
From Prithvi-EO-2.0: A Versatile Multitemporal Foundation Model for Earth Observation Applications (2026) · cites this paper as [7]
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- Authors
- Wang, Y., Albrecht, C. M., Braham, N. A. A., Liu, C., Xiong, Z., & Zhu, X. X.
- Affiliations
- Deutsches Zentrum für Luft- und Raumfahrt e. V. (DLR) (DE)
- Technical University of Munich (DE)
- Munich Center for Machine Learning (DE)
- Corresponding author
- Wang, Yi
- Venue
- Lecture Notes in Computer Science
- Publisher
- Springer Nature Switzerland
- Published
- 2024-11-03
- Pages
- 286-303
- ISSN
- 0302-9743 (ISSN-L)
- 1611-3349
- OpenAlex
- W4403998581
- Cited by
- 49
- References
- 44
- Citations by year
- 2026: 19 · 2025: 27 · 2024: 3
- FWCI
- 10.13
- Type
- conference paper
- Language
- en
- Open access
- green
- arxiv.org
- Licence
- Locations
- Lecture notes in computer science (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- elib (German Aerospace Center) (submittedVersion)
- arXiv (Cornell University)
- Funders
- European Commission (101082130, 101130544, HORIZON2020)
- Bundesministerium für Bildung und Forschung (01DD20001)
- Helmholtz Artificial Intelligence Cooperation Unit (ZT-I-PF-5-01)
- Bundesministerium für Umwelt, Naturschutz, nukleare Sicherheit und Verbraucherschutz (67KI32002B;)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Multimodal Machine Learning Applications
- Physical Sciences › Computer Science › Artificial Intelligence › Domain Adaptation and Few-Shot Learning
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
- Keywords
- RGB-D fusion
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
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