In this paper, we address an open-set action recognition problem. While the closed-set action recognition classifies test samples into the same classes of actions used for model training, the problem of the open-set action recognition is more challenging because there is a possibility that the trained model has to recognize actions which do not appear in the training set. To address this issue, we propose an action matching network (AMN) that can identify and classify both actions in the training dataset and the actions not included in the set. AMN extracts spatio-temporal representations from the given video clips and constructs an action dictionary using the given samples. Then, AMN classifies an action by computing the similarity based on Euclidean distance or generates a new action class in the constructed dictionary if it is necessary. Experimental results on UCF101 dataset and a large human motion dataset (a.k.a., HMDB dataset) demonstrate the benefits of AMN over the state-of-the-art approaches to open-set action recognition problems.

中文翻译：

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动作匹配网络：使用时空表示匹配的开放集动作识别

在本文中，我们解决了一个开放集动作识别问题。虽然闭集动作识别将测试样本分类为用于模型训练的相同动作类别，但开集动作识别的问题更具挑战性，因为训练后的模型可能必须识别未出现的动作在训练集中。为了解决这个问题，我们提出了一个动作匹配网络（AMN），它可以识别和分类训练数据集中的动作和未包含在集合中的动作。AMN 从给定的视频剪辑中提取时空表示，并使用给定的样本构建一个动作字典。然后，AMN 通过基于欧几里德距离计算相似度对动作进行分类，或者在必要时在构造的字典中生成新的动作类。