论文标题
使用基于暹罗网络的深度度量学习的行动质量评估
Action Quality Assessment using Siamese Network-Based Deep Metric Learning
论文作者
论文摘要
基于自动化的分数估计模型可以用作避免判断偏见的替代意见。过去的工作中，得分估计模型是通过将视频表示形式回归给法官提供的地面真实分数来学习的。但是，基于回归的解决方案在给出奖励分数的原因方面缺乏解释性。使得分数更加明确的一种解决方案是将给定的动作视频与参考视频进行比较。这将捕获时间变化W.R.T.参考视频并将这些变体映射到最终分数。在这项工作中，我们提出了一个新的动作评分系统作为两阶段的系统：（1）一个深度度量学习模块，该模块根据法官给出的基于他们的基础真实分数来学习任何两个动作视频之间的相似性； （2）使用第一个模块查找视频与参考视频的相似度以给出评估得分的分数估计模块。拟议的评分模型已经测试了奥运会潜水和体操库，该模型的表现优于现有的最新评分模型。
Automated vision-based score estimation models can be used as an alternate opinion to avoid judgment bias. In the past works the score estimation models were learned by regressing the video representations to the ground truth score provided by the judges. However such regression-based solutions lack interpretability in terms of giving reasons for the awarded score. One solution to make the scores more explicable is to compare the given action video with a reference video. This would capture the temporal variations w.r.t. the reference video and map those variations to the final score. In this work, we propose a new action scoring system as a two-phase system: (1) A Deep Metric Learning Module that learns similarity between any two action videos based on their ground truth scores given by the judges; (2) A Score Estimation Module that uses the first module to find the resemblance of a video to a reference video in order to give the assessment score. The proposed scoring model has been tested for Olympics Diving and Gymnastic vaults and the model outperforms the existing state-of-the-art scoring models.
