论文标题
异步相互作用聚集以进行动作检测
Asynchronous Interaction Aggregation for Action Detection
论文作者
论文摘要
了解互动是视频动作检测的重要组成部分。我们提出了异步相互作用聚集网络（AIA），该网络利用不同的相互作用来增强动作检测。其中有两个关键设计：一个是相互作用聚集结构（IA）采用统一范式来建模和整合多种类型的相互作用。另一个是异步内存更新算法（AMU），它使我们能够通过动态地进行长期建模而没有巨大的计算成本来实现更好的性能。我们提供经验证据，以表明我们的网络可以从综合互动中获得明显的准确性，并且易于端到端训练。我们的方法报告了AVA数据集上新的最先进的性能，与我们的强基线相比，验证拆分的3.7 MAP增益（相对改进）为3.7。数据集UCF101-24和Epic-Kitchens的结果进一步说明了我们方法的有效性。源代码将在以下网址公开：https：//github.com/mvig-sjtu/alphaction。
Understanding interaction is an essential part of video action detection. We propose the Asynchronous Interaction Aggregation network (AIA) that leverages different interactions to boost action detection. There are two key designs in it: one is the Interaction Aggregation structure (IA) adopting a uniform paradigm to model and integrate multiple types of interaction; the other is the Asynchronous Memory Update algorithm (AMU) that enables us to achieve better performance by modeling very long-term interaction dynamically without huge computation cost. We provide empirical evidence to show that our network can gain notable accuracy from the integrative interactions and is easy to train end-to-end. Our method reports the new state-of-the-art performance on AVA dataset, with 3.7 mAP gain (12.6% relative improvement) on validation split comparing to our strong baseline. The results on dataset UCF101-24 and EPIC-Kitchens further illustrate the effectiveness of our approach. Source code will be made public at: https://github.com/MVIG-SJTU/AlphAction .
