Using objective ground-truth labels created by multiple annotators for improved video classification: A comparative study
We present a strategy to create objectively labeled ground-truth set of videos. Such a strategy mitigates the subjective biases of the annotation process. Objective labels show superior consistency than subjective labels. A classifier is trained to predict objective labels for 51K unlabeled videos.
video classification large dataset analysis data annotation