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No Need to Sacrifice Data Quality for Quantity: Crowd-Informed Machine Annotation for Cost-Effective Understanding of Visual Data

Fuente: arXiv

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Dettagli Bibliografici
Autori principali:	Klugmann, Christopher, Mahmood, Rafid, Hegde, Guruprasad, Kale, Amit, Kondermann, Daniel
Natura:	Preprint
Pubblicazione:	2024
Soggetti:	Human-Computer Interaction Computer Vision and Pattern Recognition Machine Learning
Accesso online:	Acceder al recurso
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