To Be Multimodal or Not to Be: Query-Adaptive Audio-Visual Person Retrieval via Active Modality Detection
arXiv:2606.05931v1 Announce Type: new Abstract: When retrieving a person from a video archive by voice and face, should the system be multimodal or not? In real-world broadcast archives, unlike curated benchmarks, a target may be heard but unseen, seen but unheard, or both. Fusing scores from an absent modality injects noise, degrading precision below the best unimodal system. We propose a query-adaptive framework that detects active modalities via cross-modal score consistency: when both modalities are active, files retrieved by one also score highly on the other; this agreement breaks down w
The proliferation of video archives and advanced AI capabilities makes efficient and accurate multimodal retrieval increasingly crucial, pushing the boundaries of existing systems.
This research addresses a fundamental challenge in AI agent reliability and performance by optimizing multimodal data fusion, directly impacting the precision of systems operating with diverse data streams.
AI systems can now dynamically adapt to the presence or absence of specific modalities (like audio or visual) in real-world messy data, leading to more robust retrieval without human intervention.
- · AI developers
- · Video archive managers
- · Security and surveillance sectors
- · AI agent designers
- · Systems relying on naive multimodal fusion
- · Inefficient data retrieval methods
More accurate and efficient person retrieval within large video datasets.
Improved performance and reliability of AI agents and autonomous systems that process multimodal information.
Enhanced AI capabilities for critical applications in policing, intelligence, and media analysis through advanced multimodal understanding.
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Read at arXiv cs.CL