Predicting which specific parts of a video users will replay is important for
several applications, including targeted advertisement placement on video
platforms and assisting video creators. In this work, we explore whether it is
possible to predict the Most Replayed (MR) data from YouTube videos. To this
end, we curate a large video benchmark, the YTMR500 dataset, which comprises
500 YouTube videos with MR data annotations. We evaluate Deep Learning (DL)
models of varying complexity on our dataset and perform an extensive ablation
study. In addition, we conduct a user study to estimate the human performance
on MR data prediction. Our results show that, although by a narrow margin, all
the evaluated DL models outperform random predictions. Additionally, they
exceed human-level accuracy. This suggests that predicting the MR data is a
difficult task that can be enhanced through the assistance of DL. Finally, we
believe that DL performance on MR data prediction can be further improved, for
example, by using multi-modal learning. We encourage the research community to
use our benchmark dataset to further investigate automatic MR data prediction.

利用深度学习模型对 YouTube 视频的 MR（Most Replayed）数据进行预测，并通过评估多个模型在 YTMR500 数据集上的性能，显示出这是困难的任务，但所有模型都优于随机预测，并且超过了人类水平准确度。鼓励研究社区使用我们的基准数据集进一步研究自动 MR 数据预测。