Multimedia-Aware Question Answering: A Review of Retrieval and Cross-Modal Reasoning Architectures
Abstract
Question Answering (Q\A) 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 Q\A 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 Q&A systems leveraging multimedia data.
Read the paper
Related studies
- Knowledge Management Perspective of Generative Artificial Intelligence
- Navigating Generative Artificial Intelligence Promises and Perils for Knowledge and Creative Work
- Characterizing the evolving research on enterprise content management
- Large language models and multimodal foundation models for precision oncology
- From Generative AI to Extended Reality: Multidisciplinary Perspectives on the Challenges, Opportunities, and Future of Educational Computing
- Generative Artificial Intelligence: A New Engine for Advancing Environmental Science and Engineering
Go deeper in Evidence Hub
Evidence Hub adds a study data sheet (design, sample size, evidence level, key numbers), an AI summary and a chat grounded in the paper's text.