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Existing self-supervised learning approaches are mainly designed for natural images.
“existing self-supervised learning approaches [1, 2, 3, 4, 5, 6] are mainly designed for natural images.”
From SatMAE: Pre-Training Transformers for Temporal and Multi-Spectral Satellite Imagery (2022) · cites this paper as [2]
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- Authors
- Tian, Y., Krishnan, D., & Isola, P.
- Affiliations
- MIT Computer Science and Artificial Intelligence Laboratory (US)
- Google (United States) (US)
- Corresponding author
- Tian, Yonglong
- Venue
- Lecture Notes in Computer Science
- Publisher
- Springer International Publishing
- Published
- 2020
- Pages
- 776-794
- ISSN
- 0302-9743 (ISSN-L)
- 1611-3349
- OpenAlex
- W3108655343
- Cited by
- 1,997
- References
- 9
- Citations by year
- 2026: 107 · 2025: 288 · 2024: 367 · 2023: 411 · 2022: 422 · 2021: 338 · 2020: 60 · 2019: 3
- FWCI
- 93.53
- Type
- conference paper
- Language
- en
- Open access
- closed
- Licence
- Locations
- Lecture notes in computer science (publishedVersion)
- 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 Image and Video Retrieval Techniques
- Keywords
- view-invariant representation, multimodal representation learning, scene semantics, contrastive loss
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