Abstract:Large annotated datasets are crucial for training deep machine learning models, but they are expensive and time-consuming to create. There are already numerous public datasets, but a vast amount of unlabeled data, especially video data, can still be annotated and leveraged to further improve the performance and accuracy of machine learning models. Therefore, it is essential to reduce the time and effort required to annotate a dataset to prevent bottlenecks in the development of this field. In this study, we pr… Show more
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