Deep Learning Approaches for Multimodal Intent Recognition: A Survey

TL;DR

This survey reviews recent deep learning methods for multimodal intent recognition, highlighting the shift from unimodal to multimodal approaches, the impact of Transformer models, and future research directions in natural human-computer interaction.

Contribution

It provides a comprehensive overview of deep learning techniques for multimodal intent recognition, including datasets, methodologies, and challenges, with a focus on recent Transformer-based breakthroughs.

Findings

Transformer models have significantly advanced MIR.

Multimodal approaches outperform unimodal methods.

Current challenges include data integration and model interpretability.

Abstract

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.