Abstract
Despite the recent progress in video understanding and the continuous rate of improvement in temporal action localization throughout the years, it is still unclear how far (or close?) we are to solving the problem. To this end, we introduce a new diagnostic tool to analyze the performance of temporal action detectors in videos and compare different methods beyond a single scalar metric. We exemplify the use of our tool by analyzing the performance of the top rewarded entries in the latest ActivityNet action localization challenge. Our analysis shows that the most impactful areas to work on are: strategies to better handle temporal context around the instances, improving the robustness w.r.t. the instance absolute and relative size, and strategies to reduce the localization errors. Moreover, our experimental analysis finds the lack of agreement among annotator is not a major roadblock to attain progress in the field. Our diagnostic tool is publicly available to keep fueling the minds of other researchers with additional insights about their algorithms.
Abstract (translated)
尽管最近视频理解方面取得了进展，并且多年来时间动作本地化的持续改进速度，但我们还不清楚解决问题的距离（或接近？）。为此，我们引入了一种新的诊断工具来分析视频中时间动作检测器的性能，并比较单个标量度量之外的不同方法。我们通过分析最新ActivityNet行动本地化挑战中最高奖励条目的表现来举例说明我们工具的使用。我们的分析表明，最有影响力的领域是：更好地处理实例周期背景的策略，提高w.r.t的鲁棒性。实例绝对和相对大小，以及减少本地化错误的策略。此外，我们的实验分析发现，注释者之间缺乏一致性并不是在该领域取得进展的主要障碍。我们的诊断工具可以公开获取，以便通过对算法的更多见解来激励其他研究人员。
URL
https://arxiv.org/abs/1807.10706
