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

Rahul Raja, Arpita Vats

TL;DR

Multimedia QA addresses the challenge of answering questions by leveraging heterogeneous multimedia evidence across text, images, video, and audio, with cross-modal alignment and temporal reasoning as central goals. The paper surveys retrieval-augmented architectures, detailing dense and embedding retrieval, cross-modal fusion, and LLM-based pipelines, while outlining taxonomy, architectures, benchmark considerations, and practical trade-offs. Its contributions include a structured taxonomy of modalities and retrieval strategies, a synthesis of representative architectures and benchmarks, and a discussion of open challenges and future directions for robust, explainable multimedia QA. The work aims to guide researchers and practitioners in building scalable, interpretable QA systems that operate over live, multimedia content and provide contextually grounded, temporally aware responses.

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.

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

TL;DR

Multimedia QA addresses the challenge of answering questions by leveraging heterogeneous multimedia evidence across text, images, video, and audio, with cross-modal alignment and temporal reasoning as central goals. The paper surveys retrieval-augmented architectures, detailing dense and embedding retrieval, cross-modal fusion, and LLM-based pipelines, while outlining taxonomy, architectures, benchmark considerations, and practical trade-offs. Its contributions include a structured taxonomy of modalities and retrieval strategies, a synthesis of representative architectures and benchmarks, and a discussion of open challenges and future directions for robust, explainable multimedia QA. The work aims to guide researchers and practitioners in building scalable, interpretable QA systems that operate over live, multimedia content and provide contextually grounded, temporally aware responses.

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.
Paper Structure (12 sections, 12 equations, 4 figures)

This paper contains 12 sections, 12 equations, 4 figures.

Table of Contents

  1. Introduction
  2. Taxonomy of Multimedia QA Systems
  3. Modality-Specific QA Systems
  4. Task-Oriented QA Systems
  5. Multimodal Retrieval Strategies for QA Systems
  6. Dense Retrieval
  7. Embedding Retrieval
  8. Cross-Modal Retrieval
  9. Temporal Video Segment Retrieval
  10. Audio-Visual Retrieval
  11. Multimodal QA Architectures and Benchmarks
  12. Conclusion

Figures (4)

  • Figure 1: A hierarchical taxonomy of Multimedia QA systems categorized by input modality, task formulation, and retrieval strategy, highlighting key models and reasoning approaches across heterogeneous data types.
  • Figure 2: VideoBERT jointly models text and video by learning cross-modal representations with masked token prediction across both modalities sun2019videobertjointmodelvideo.
  • Figure 3: RETRO Architecture.Left: A simplified illustration where an input sequence of length $n = 12$ is divided into $l = 3$ chunks, each containing $m = 4$ tokens. For every chunk, $k = 2$ nearest-neighbor segments are retrieved, each consisting of $r = 5$ tokens. The retrieval pathway is depicted above the sequence. Right: A closer view of the interactions within the CCA operator. Causal structure is preserved: the neighbors retrieved for the first chunk influence only the final token of that chunk and the tokens in the subsequent chunk borgeaud2022improvinglanguagemodelsretrieving.
  • Figure 4: Overview of the CLIPBERT architecture. The diagram illustrates prediction for a single sampled clip. When multiple clips are sampled, their individual predictions are aggregated to produce the final result. lei2021less.