在线动作检测的时间建模方法研究
A Comprehensive Study on Temporal Modeling for Online Action Detection
摘要：
在线动作检测是一个实用而又具有挑战性的课题，近年来受到越来越多的关注。典型的OAD系统主要由三个模块组成：帧级特征提取器，其通常基于预训练的深度卷积神经网络（CNN）、时间建模模块和动作分类器。其中，时序建模模块是关键，它从历史和当前特征中聚合有区别的信息。虽然人们已经为OAD等课题开发了多种时态建模方法，但对它们在OAD中的应用效果还缺乏足够的研究。本文的目的是提供一个全面的研究OAD的时间建模，包括四个Meta类型的时间建模方法，\ie时间池，时间卷积，递归神经网络和时间注意力，并揭示一些良好的做法，产生一个国家的最先进的OAD系统。其中许多是首次在OAD中探索的，并使用各种超参数进行了广泛的评估。此外，基于我们的全面研究，我们提出了几种混合时间建模方法，这些方法在THUMOS-14和TVSeries上的表现优于最近的最先进的方法，具有相当大的利润率。
英文摘要：
Online action detection (OAD) is a practical yet challenging task, which has attracted increasing attention in recent years. A typical OAD system mainly consists of three modules: a frame-level feature extractor which is usually based on pre-trained deep Convolutional Neural Networks (CNNs), a temporal modeling module, and an action classifier. Among them, the temporal modeling module is crucial which aggregates discriminative information from historical and current features. Though many temporal modeling methods have been developed for OAD and other topics, their effects are lack of investigation on OAD fairly. This paper aims to provide a comprehensive study on temporal modeling for OAD including four meta types of temporal modeling methods, \ie temporal pooling, temporal convolution, recurrent neural networks, and temporal attention, and uncover some good practices to produce a state-of-the-art OAD system. Many of them are explored in OAD for the first time, and extensively evaluated with various hyper parameters. Furthermore, based on our comprehensive study, we present several hybrid temporal modeling methods, which outperform the recent state-of-the-art methods with sizable margins on THUMOS-14 and TVSeries.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
