Multimodal Video Understanding: A Capability-Based Survey of Alignment, Expression, and Reasoning

Abstract

Multimodal video understanding (MVU) has emerged as a fast-growing research frontier, driven by major advances in video-language pre-training and large multimodal models over the past decade. MVU aims to synergistically integrate visual, audio and textual modalities to interpret complex video semantics, supporting widespread downstream tasks including cross-modal retrieval, dense captioning, video question answering, event analysis and intelligent assistance. Despite the rapid proliferation of specialized MVU models, the community still lacks a unified capability-centric framework to systematically clarify the hierarchical competency architecture and evolutionary trajectory of state-of-the-art approaches. To address this issue, this paper presents a structured, comprehensive survey of the latest MVU progress, establishing a novel three-tier taxonomy that categorizes existing studies into cross-modal alignment, multi-granularity semantic expression and multimodal reasoning. Along this pipeline, we further systematically synthesize core modality fusion strategies, mainstream benchmark datasets and standardized evaluation protocols. Through a fine-grained analysis of representative published results, we highlight the critical impact of inconsistent evaluation settings, cross-experiment comparability bottlenecks and inherent methodological trade-offs between performance and efficiency. Finally, we identify and dissect three key open challenges: ultra-long video scalability, performance degradation from modality noise and missing data, and factual reliability risks in generative MVU systems. This capability-oriented systematic reference clarifies the methodological evolution logic of MVU, and provides actionable guidance for developing next-generation robust, high-performance multimodal video understanding systems.