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Fine-Grained Multimodal Alignment

Establish robust fine-grained cross-modal alignment techniques for multimedia question answering systems that accurately synchronize spoken language (e.g., ASR transcripts or audio) with corresponding visual scenes to enable precise grounding and reasoning across modalities.

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Background

The survey emphasizes that effective multimedia QA requires precise alignment between heterogeneous signals such as audio, text, and video. Many existing systems retrieve and fuse modality-specific features but struggle with temporal and semantic synchronization at segment-level granularity, which limits reliable evidence grounding and answer generation. Addressing fine-grained alignment is critical for tasks like video QA where spoken narration, subtitles, and visual actions must be linked in time and content.

References

Despite recent progress, several challenges remain unresolved. Key issues include the difficulty of finegrained multimodal alignment (e.g., syncing spoken language with visual scenes), the lack of robust trustworthiness mechanisms such as modality attribution or segment-level citations, and the computational overhead introduced by real time or large scale retrieval. Further complexities arise in handling multilingual queries and supporting low-resource modalities, along with the persistent challenge of evaluating answer quality across modalities.

— Multimedia-Aware Question Answering: A Review of Retrieval and Cross-Modal Reasoning Architectures (2510.20193 - Raja et al., 23 Oct 2025) in Conclusion (Section 5)