3 citations · 3 across the 7 of their papers we have counts for
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cs.HC2024
No Need to Sacrifice Data Quality for Quantity: Crowd-Informed Machine Annotation for Cost-Effective Understanding of Visual Data
Christopher Klugmann, Rafid Mahmood, Guruprasad Hegde +2
Labeling visual data is expensive and time-consuming. Crowdsourcing systems promise to enable highly parallelizable annotations through the participation of monetarily or otherwise…
cs.CV2024★ 3 cited
Ambiguous Annotations: When is a Pedestrian not a Pedestrian?
Luisa Schwirten, Jannes Scholz, Daniel Kondermann +1
Datasets labelled by human annotators are widely used in the training and testing of machine learning models. In recent years, researchers are increasingly paying attention to labe…