Vision-Based Multimodal Interfaces: A Survey and Taxonomy for Enhanced Context-Aware System Design

2025-01-23

AI Summary

Existing surveys on visual multimodal interfaces (VMIs) are predominantly task- or scenario-oriented, lacking a unified design paradigm. Method: This paper proposes a novel, vision-anchored, data-modality-driven classification and design framework, established through systematic literature review and cross-dimensional modeling. It introduces a four-dimensional taxonomy encompassing input modalities, fusion mechanisms, interaction objectives, and deployment environments, structured hierarchically as “holistic–detail–holistic.” Contribution/Results: The framework transcends conventional survey limitations by positioning the visual modality at the core of context-aware system design for the first time, integrating theories from human-computer interaction, multimodal learning, and context modeling. It delivers a reusable design methodology and principled guidelines for high-fidelity user intent understanding and seamless physical-digital interaction.

Technology Category

• Computer Vision: Multi-modal Vision
• Machine Learning: Multimodal Learning
• Data Mining & Knowledge Management: Mining of Visual, Multimedia & Multimodal Data

Application Category

• User Modeling, Personalization and Recommendation: Explainable and interpretable methods for personalization
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• Web Mining and Content Analysis: Mining multimedia, multimodal, multilingual, cross-lingual Web data

Abstract

The recent surge in artificial intelligence, particularly in multimodal processing technology, has advanced human-computer interaction, by altering how intelligent systems perceive, understand, and respond to contextual information (i.e., context awareness). Despite such advancements, there is a significant gap in comprehensive reviews examining these advances, especially from a multimodal data perspective, which is crucial for refining system design. This paper addresses a key aspect of this gap by conducting a systematic survey of data modality-driven Vision-based Multimodal Interfaces (VMIs). VMIs are essential for integrating multimodal data, enabling more precise interpretation of user intentions and complex interactions across physical and digital environments. Unlike previous task- or scenario-driven surveys, this study highlights the critical role of the visual modality in processing contextual information and facilitating multimodal interaction. Adopting a design framework moving from the whole to the details and back, it classifies VMIs across dimensions, providing insights for developing effective, context-aware systems.

Problem

• Visual Multimodal Interfaces
• Environmental Understanding
• Human-Computer Interaction

Innovation

• Visual Multimodal Interfaces
• Context-aware Computing
• Intelligent System Design