Due to its widespread applications, human action recognition is one of the
most widely studied research problems in Computer Vision. Recent studies have
shown that addressing it using multimodal data leads to superior performance as
compared to relying on a single data modality. During the adoption of deep
learning for visual modelling in the last decade, action recognition approaches
have mainly relied on Convolutional Neural Networks (CNNs). However, the recent
rise of Transformers in visual modelling is now also causing a paradigm shift
for the action recognition task. This survey captures this transition while
focusing on Multimodal Human Action Recognition (MHAR). Unique to the induction
of multimodal computational models is the process of "fusing" the features of
the individual data modalities. Hence, we specifically focus on the fusion
design aspects of the MHAR approaches. We analyze the classic and emerging
techniques in this regard, while also highlighting the popular trends in the
adaption of CNN and Transformer building blocks for the overall problem. In
particular, we emphasize on recent design choices that have led to more
efficient MHAR models. Unlike existing reviews, which discuss Human Action
Recognition from a broad perspective, this survey is specifically aimed at
pushing the boundaries of MHAR research by identifying promising architectural
and fusion design choices to train practicable models. We also provide an
outlook of the multimodal datasets from their scale and evaluation viewpoint.
Finally, building on the reviewed literature, we discuss the challenges and
future avenues for MHAR.

多模态人体动作识别的综述研究，重点关注多模态数据融合设计方面的经典和新兴技术，分析了卷积神经网络和 Transformer 建模方法在该问题上的流行趋势和更高效的模型设计选择，并讨论了实践模型训练中的有前景的架构和融合设计选择，以及多模态数据集的规模和评估视角，提出了多模态人体动作识别的挑战与未来发展方向。