To Be Multimodal or Not to Be: Query-Adaptive Audio-Visual Person Retrieval via Active Modality Detection

2026-06-04 · Source: Artificial Intelligence · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Computer Vision, Speech Processing · Depth: Expert, quick

Summary

A query-adaptive framework is proposed for audio-visual person retrieval, addressing the challenge of absent modalities in real-world video archives. Unlike curated benchmarks, broadcast content often features individuals who are heard but unseen, or seen but unheard. Fixed multimodal fusion in such scenarios injects noise, degrading precision below unimodal systems. The framework detects active modalities using cross-modal score consistency: high agreement between modalities indicates both are active, while disagreement signals an absent modality. Classifiers leveraging these cross-modal features achieve 89% detection accuracy. Evaluated on the BBC Rewind corpus (over 12,000 videos), the adaptive system attained 94.2% P@1, surpassing speaker-only (82.9%), face-only (93.4%), and fixed fusion (90.0%), recovering 64% of the gap to an oracle (96.6%).

Key takeaway

For machine learning engineers designing person retrieval systems for real-world, noisy video archives, you should avoid fixed multimodal fusion. Instead, implement query-adaptive modality detection to dynamically determine active modalities. This approach, leveraging cross-modal score consistency, will significantly improve your system's precision, as demonstrated by its superior performance over unimodal and fixed fusion methods on broadcast data.

Key insights

Query-adaptive modality detection significantly improves person retrieval by preventing noise from absent modalities.

Principles

• Fusing scores from an absent modality degrades retrieval precision.
• Cross-modal score consistency indicates active modality presence.

Method

Classifiers driven by cross-modal features detect active modalities, achieving 89% accuracy.

In practice

• Implement cross-modal score consistency checks for robust multimodal retrieval.
• Prioritize unimodal systems over fixed fusion when modality presence is uncertain.

Topics

• Audio-Visual Person Retrieval
• Multimodal Fusion
• Active Modality Detection
• Cross-modal Consistency
• Video Archives
• BBC Rewind

Best for: Research Scientist, AI Scientist, Machine Learning Engineer

Related on AIssential

• To Be Multimodal or Not to Be: Query-Adaptive Audio-Visual Person Retrieval via Active Modality Detection — Cross-modal score consistency reliably detects active modalities, preventing noise from absent modalities in multimodal retrieval.
• Two-Stage Multimodal Framework for Emotion Mimicry Intensity Prediction — Staged multimodal fusion with independent encoder pre-training improves robustness for continuous emotion intensity prediction.
• M$^3$Exam: Benchmarking Multimodal Memory for Realistic User-Agent Interactions — Realistic user-agent interactions demand multimodal memory benchmarks that evaluate cross-modal grounding and implicit inference.
• From Scenes to Elements: Multi-Granularity Evidence Retrieval for Verifiable Multimodal RAG — Element-level visual evidence retrieval significantly enhances multimodal RAG verifiability and performance.
• Conan-embedding-v3: Fusing Modality-Specific Models for Omni-Modal Embedding — Fusing modality-specific models for omni-modal embeddings requires addressing "Projector Drift" through targeted recovery.
• Towards Resolving Optimization Conflicts Between Image- and Text-Based Person Re-Identification — Decoupling I2I and T2I ReID training with a two-stage pipeline resolves optimization conflicts for better shared representations.

Open in AIssential →

Editorial summary, takeaway, and curation by AIssential. Original article published by Artificial Intelligence.