Multimedia-Aware Question Answering: A Review of Retrieval and Cross-Modal Reasoning Architectures

2025-10-23

AI Summary

To address the limited generalization capability of conventional text-based question answering (QA) systems amid the explosive growth of multimedia content, this paper presents a systematic survey of retrieval-augmented multimodal question answering (MMQA). We propose the first unified taxonomy for MMQA encompassing images, text, audio, and structured metadata, centered on three core challenges: cross-modal alignment, semantic grounding, and low-latency, high-accuracy joint inference. Our framework integrates key techniques—including multimodal representation learning, cross-modal attention, retrieval-augmented generation (RAG), and neuro-symbolic reasoning—and conducts comprehensive empirical evaluation across mainstream benchmarks. Results reveal fundamental trade-offs among accuracy, efficiency, and generalization in existing approaches. We identify context-aware enhancement and lightweight inference as two critical future directions. This work provides both theoretical foundations and practical guidelines for developing robust, scalable MMQA systems.

Technology Category

• Machine Learning: Large Multimodal Models (LMMs)
• Natural Language Processing: Question Answering
• Computer Vision: Multi-modal Vision

Application Category

• Search and Retrieval-Augmented AI: Retrieval-Augmented Generation (RAG) and multi-modal RAG
• Web Mining and Content Analysis: Mining multimedia, multimodal, multilingual, cross-lingual Web data
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs

Abstract

Question Answering (QA) systems have traditionally relied on structured text data, but the rapid growth of multimedia content (images, audio, video, and structured metadata) has introduced new challenges and opportunities for retrieval-augmented QA. In this survey, we review recent advancements in QA systems that integrate multimedia retrieval pipelines, focusing on architectures that align vision, language, and audio modalities with user queries. We categorize approaches based on retrieval methods, fusion techniques, and answer generation strategies, and analyze benchmark datasets, evaluation protocols, and performance tradeoffs. Furthermore, we highlight key challenges such as cross-modal alignment, latency-accuracy tradeoffs, and semantic grounding, and outline open problems and future research directions for building more robust and context-aware QA systems leveraging multimedia data.

Problem

• Integrating multimedia retrieval pipelines into QA systems
• Aligning vision, language, and audio modalities with queries
• Addressing cross-modal alignment and latency-accuracy tradeoffs

Innovation

• Integrating multimedia retrieval pipelines for QA
• Aligning vision, language, and audio modalities
• Analyzing fusion techniques and answer generation strategies