学习得分的花样滑冰运动视频
Learning to score the figure skating sports videos
摘要：
本文以花样滑冰运动视频的学习评分为研究对象。为了解决这一任务，我们提出了一个深度架构，包括两个互补的组件，即，Self-Attentive LSTM和Multi-scale Convolutional Skip LSTM。这两个组件可以有效地学习每个视频中的局部和全局序列信息。在此基础上，本文提出了一个大规模的花样滑冰运动视频数据集-- FisV数据集。该数据集包括500个花样滑冰视频，平均长度为2分50秒。每个视频由九个不同裁判的两个分数注释，即，要素总得分（TES）和项目总得分（PCS）。我们提出的模型在FisV和MIT-skate数据集上进行了验证。实验结果表明，我们的模型在学习花样滑冰视频评分的有效性。
英文摘要：
This paper targets at learning to score the figure skating sports videos. To address this task, we propose a deep architecture that includes two complementary components, i.e., Self-Attentive LSTM and Multi-scale Convolutional Skip LSTM. These two components can efficiently learn the local and global sequential information in each video. Furthermore, we present a large-scale figure skating sports video dataset -- FisV dataset. This dataset includes 500 figure skating videos with the average length of 2 minutes and 50 seconds. Each video is annotated by two scores of nine different referees, i.e., Total Element Score(TES) and Total Program Component Score (PCS). Our proposed model is validated on FisV and MIT-skate datasets. The experimental results show the effectiveness of our models in learning to score the figure skating videos.
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