Most existing video action recognition methods mainly rely on high-level semantic information from convolutional neural networks (CNNs) but ignore the discrepancies of different information streams. However, it does not normally consider both long-distance aggregations and short-range motions. Thus, to solve these problems, we propose hierarchical excitation aggregation and disentanglement networks (Hi-EADNs), which include multiple frame excitation aggregation (MFEA) and a feature squeeze-and-excitation hierarchical disentanglement (SEHD) module. MFEA specifically uses long-short range motion modelling and calculates the feature-level temporal difference. The SEHD module utilizes these differences to optimize the weights of each spatiotemporal feature and excite motion-sensitive channels. Moreover, without introducing additional parameters, this feature information is processed with a series of squeezes and excitations, and multiple temporal aggregations with neighbourhoods can enhance the interaction of different motion frames. Extensive experimental results confirm our proposed Hi-EADN method effectiveness on the UCF101 and HMDB51 benchmark datasets, where the top-5 accuracy is 93.5% and 76.96%.

中文翻译：

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Hi-EADN：基于视频的动作识别的分层激励聚合和解缠解框架

大多数现有的视频动作识别方法主要依赖于卷积神经网络（CNN）的高级语义信息，但忽略了不同信息流的差异。但是，它通常不会同时考虑长距离聚合和短距离运动。因此，为解决这些问题，我们提出了分层激励聚合和解缠结网络（Hi-EADN），其中包括多帧激励聚合（MFEA）和特征挤压和激励分层解缠结（SEHD）模块。MFEA特别使用长短距离运动建模并计算特征级时差。SEHD模块利用这些差异来优化每个时空特征的权重并激发运动敏感通道。而且，在不引入其他参数的情况下，通过一系列挤压和激励来处理此特征信息，并且具有邻域的多个时间聚合可以增强不同运动帧的交互。大量的实验结果证实了我们提出的Hi-EADN方法在UCF101和HMDB51基准数据集上的有效性，前5位的准确度分别为93.5％和76.96％。