Skeleton-based action recognition receives the attention of many researchers as it is robust to viewpoint and illumination changes, and its processing is much more efficient than video frames. With the emergence of deep learning models, it has become very popular to represent the skeleton data in pseudo-image form and apply Convolutional Neural Networks for action recognition. Thereafter, studies concentrated on finding effective methods for forming pseudo-images. Recently, attention networks, more specifically transformers have provided promising results in various vision problems. In this study, the effectiveness of vision transformers for skeleton-based action recognition is examined and its robustness on the pseudo-image representation scheme is investigated. To this end, a three-level architecture, Act-VIT is proposed, which forms a set of pseudo images apply a classifier on each of the representation and combine their results to find the final action class. The classifiers of Act-VIT are first realized by CNNs and then by VITs and their performances are compared. Experimental studies reveal that the vision transformer is less sensitive to the initial pseudo-image representation compared to CNN. Nevertheless, even with the vision transformer, the recognition performance can be further improved by consensus of classifiers.

本文研究了基于骨架的动作识别中视觉transformers的有效性以及其在伪图像表示方案上的鲁棒性，并提出了一个三级体系结构Act-VIT，通过应用分类器，在每个表示上合并其结果以找到最终的动作类别。实验研究表明，相比于CNN，视觉transformers对初始伪图像表示的敏感性较低，但是通过分类器的一致性仍然可以进一步提高识别性能。

Act-VIT：使用Vision Transformer进行基于骨架的动作识别的表征稳健注意力架构