Towards Recognizing Unseen Categories in Unseen Domains
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CL has already demonstrated an improved performance in medical image classification tasks, such as thoracic disease, skin disease, proximal femur fractures and breast screening classification.
“CL has already demonstrated an improved performance in medical image classification tasks, such as thoracic disease [36], skin disease [37], proximal femur fractures [12], [38] and breast screening classification [39].”
From Memory-aware curriculum federated learning for breast cancer classification (2023) · cites this paper as [37]
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- Authors
- Mancini, M., Akata, Z., Ricci, E., & Caputo, B.
- Affiliations
- University of Tübingen (DE)
- Max Planck Society (DE)
- University of Trento (IT)
- Corresponding author
- Mancini, Massimiliano
- Venue
- Lecture Notes in Computer Science
- Publisher
- Springer International Publishing
- Published
- 2020
- Pages
- 466-483
- ISSN
- 0302-9743 (ISSN-L)
- 1611-3349
- OpenAlex
- W3044650432
- Cited by
- 9
- References
- 59
- Citations by year
- 2025: 1 · 2024: 2 · 2023: 2 · 2021: 3 · 2020: 1
- FWCI
- 0.57
- Type
- conference paper
- Language
- en
- Open access
- green
- arxiv.org
- Licence
- Locations
- Lecture notes in computer science (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- MPG.PuRe (Max Planck Society) (submittedVersion)
- arXiv (Cornell University)
- Funders
- Deutsche Forschungsgemeinschaft (206/4-1, 390727645)
- Topics
- 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
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Image and Video Retrieval Techniques
- Keywords
- zero-shot learning, domain generalization, domain adaptation, unseen categories, unseen domains, semantic change, domain shift, curriculum learning, mixup, visual recognition
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