论文标题
时间动作细分：现代技术的分析
Temporal Action Segmentation: An Analysis of Modern Techniques
论文作者
论文摘要
视频中的时间动作细分（TAS）旨在密集地识别长达几分钟的视频中的视频帧，并具有多个动作类别。作为一项远程视频理解任务，研究人员开发了扩展的方法集合，并使用各种基准检查了其性能。尽管近年来TAS技术的增长迅速，但这些领域尚未进行系统的调查。这项调查分析并总结了最重要的贡献和趋势。特别是，我们首先检查任务定义，常见的基准，监督类型和普遍的评估措施。此外，我们系统地研究了该主题的两种基本技术，即框架表示和时间建模，这些技术已经在文献中进行了广泛研究。然后，我们对现有的TAS作品进行了彻底的审查，该作品按其监督水平分类，并通过识别和强调几个研究差距来结束我们的调查。此外，我们已经策划了TAS资源列表，该清单可在https://github.com/nus-cvml/awsome-temporal-action-actementation上找到。
Temporal action segmentation (TAS) in videos aims at densely identifying video frames in minutes-long videos with multiple action classes. As a long-range video understanding task, researchers have developed an extended collection of methods and examined their performance using various benchmarks. Despite the rapid growth of TAS techniques in recent years, no systematic survey has been conducted in these sectors. This survey analyzes and summarizes the most significant contributions and trends. In particular, we first examine the task definition, common benchmarks, types of supervision, and prevalent evaluation measures. In addition, we systematically investigate two essential techniques of this topic, i.e., frame representation and temporal modeling, which have been studied extensively in the literature. We then conduct a thorough review of existing TAS works categorized by their levels of supervision and conclude our survey by identifying and emphasizing several research gaps. In addition, we have curated a list of TAS resources, which is available at https://github.com/nus-cvml/awesome-temporal-action-segmentation.
