Deep Learning Approaches for Multimodal Intent Recognition: A Survey
It provides researchers with insights into the latest developments in multimodal intent recognition, but it is incremental as it is a survey paper.
This survey tackles the problem of intent recognition by reviewing the evolution from unimodal to multimodal deep learning approaches, including Transformer-based models, and covers datasets, methodologies, applications, and challenges in the field.
Intent recognition aims to identify users' underlying intentions, traditionally focusing on text in natural language processing. With growing demands for natural human-computer interaction, the field has evolved through deep learning and multimodal approaches, incorporating data from audio, vision, and physiological signals. Recently, the introduction of Transformer-based models has led to notable breakthroughs in this domain. This article surveys deep learning methods for intent recognition, covering the shift from unimodal to multimodal techniques, relevant datasets, methodologies, applications, and current challenges. It provides researchers with insights into the latest developments in multimodal intent recognition (MIR) and directions for future research.