From CNNs to Transformers in Multimodal Human Action Recognition: A Survey

TL;DR

This survey reviews the transition from CNNs to Transformers in multimodal human action recognition, emphasizing fusion techniques, recent design trends, and future research directions in the field.

Contribution

It uniquely focuses on fusion design and recent architectural choices in MHAR, providing insights beyond broad human action recognition surveys.

Findings

Analysis of classic and emerging fusion techniques

Identification of recent efficient MHAR model designs

Discussion of multimodal datasets and evaluation methods

Abstract

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.