Works by Tzimiropoulos, Georgios
Results: 12
Erratum to: Deep machine learning provides state-of-the-art performance in image-based plant phenotyping.
- Published in:
- 2018
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- Publication type:
- Correction Notice
Deep machine learning provides state-of-the-art performance in image-based plant phenotyping.
- Published in:
- GigaScience, 2017, v. 6, n. 10, p. 1, doi. 10.1093/gigascience/gix083
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- Article
Changes in Dairy Cow Behavior with and without Assistance at Calving.
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- Agriculture; Basel, 2021, v. 11, n. 8, p. 722, doi. 10.3390/agriculture11080722
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- Article
Changes in Sheep Behavior before Lambing.
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- Agriculture; Basel, 2021, v. 11, n. 8, p. 715, doi. 10.3390/agriculture11080715
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- Article
Detecting Dairy Cow Behavior Using Vision Technology.
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- Agriculture; Basel, 2021, v. 11, n. 7, p. 675, doi. 10.3390/agriculture11070675
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- Article
Facial Emotion Recognition Using Transfer Learning in the Deep CNN.
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- Electronics (2079-9292), 2021, v. 10, n. 9, p. 1036, doi. 10.3390/electronics10091036
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- Article
Deep Learning Algorithms for Single Image Super-Resolution: A Systematic Review.
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- Electronics (2079-9292), 2021, v. 10, n. 7, p. 867, doi. 10.3390/electronics10070867
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- Article
Knowledge Distillation Meets Open-Set Semi-supervised Learning.
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- International Journal of Computer Vision, 2025, v. 133, n. 1, p. 315, doi. 10.1007/s11263-024-02192-7
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- Article
One-Shot Neural Face Reenactment via Finding Directions in GAN's Latent Space.
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- International Journal of Computer Vision, 2024, v. 132, n. 8, p. 3324, doi. 10.1007/s11263-024-02018-6
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- Article
Language-Aware Soft Prompting: Text-to-Text Optimization for Few- and Zero-Shot Adaptation of V &L Models.
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- International Journal of Computer Vision, 2024, v. 132, n. 4, p. 1108, doi. 10.1007/s11263-023-01904-9
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- Article
Fast Algorithms for Fitting Active Appearance Models to Unconstrained Images.
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- International Journal of Computer Vision, 2017, v. 122, n. 1, p. 17, doi. 10.1007/s11263-016-0950-1
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- Article
Euler Principal Component Analysis.
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- International Journal of Computer Vision, 2013, v. 101, n. 3, p. 498, doi. 10.1007/s11263-012-0558-z
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- Publication type:
- Article