This paper aims 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. The codes and datasets would be downloaded from https://github.com/loadder/MS_LSTM.git.

中文翻译：

---

学习得分花样滑冰运动视频

本文旨在学习对花样滑冰运动视频进行评分。为了解决这个任务，我们提出了一个深度架构，它包括两个互补的组件，即自我注意 LSTM 和多尺度卷积跳过 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。此外，我们提出了一个大规模的花样滑冰运动视频数据集——FisV 数据集。该数据集包含 500 个平均时长为 2 分 50 秒的花样滑冰视频。每个视频都由九个不同裁判的两个分数进行注释，即总元素分数（TES）和总程序组件分数（PCS）。我们提出的模型在 FisV 和 MIT-skate 数据集上得到了验证。实验结果表明我们的模型在学习对花样滑冰视频进行评分方面的有效性。