AI RESEARCH ANALYSIS
OmniVideo-R1: Reinforcing Audio-visual Reasoning with Query Intention and Modality Attention
Existing omnivideo models struggle with modality bias in audio-visual understanding. OmniVideo-R1 introduces a novel reinforced framework, leveraging query-intensive grounding and modality-attentive fusion to achieve superior mixed-modality reasoning and robust generalization.
Executive Impact: Unleashing Multimodal AI Capabilities
OmniVideo-R1 significantly advances audio-visual AI, offering enterprises enhanced accuracy and reasoning. This translates into superior performance in complex media analysis, decision support, and automation across diverse applications.
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Enhanced Cross-Modal Reasoning
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Outperformance Against SOTA
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Leading Daily-Omni Accuracy
Deep Analysis & Enterprise Applications
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Reinforced Multimodal Reasoning Framework
OmniVideo-R1 employs a novel Reinforcement Learning (RL) framework to overcome modality bias in multimodal models. It optimizes for robust reasoning behaviors by actively selecting and fusing information across audio and visual modalities.
This approach moves beyond simple dataset balancing, instilling a deeper understanding of query intention and modality attention without relying on extensive process-level annotations.
Query-Intensive Grounding (QI)
This strategy, inspired by 'think with images,' enables the model to explicitly localize and reason over relevant audio-visual segments before generating a response.
It uses a self-supervised training scheme with multiple time-caption pairs. The model generates grounding hypotheses and validates them against textual descriptions, enforced through consistency (`rcons`) and completeness (`rcomp`) rewards.
This ensures the model infers underlying intentions and extracts task-relevant cues from audio-visual content.
Modality-Attentive Fusion (MA)
Modality-Attentive Fusion maximizes the utilization of audio-visual cues, specifically addressing limitations where models overlook subtle sound cues.
A contrastive learning-based strategy encourages the model to achieve higher confidence from mixed audio-visual inputs compared to single-modality counterparts. This forces the model to discover synergistic relationships, ensuring fused representations are superior to their constituent parts, guided by the attention reward (`rattn`).
Robust Data Preparation Pipeline
A three-stage refinement pipeline is crucial for curating high-quality audio-visual data, ensuring robust training for complex reasoning tasks.
Stage 1: Quality Assessment uses Gemini-2.5-Pro to score samples on video/audio dependency, question logic, and response accuracy.
Stage 2: Heuristic Filtering discards samples not meeting strict criteria (e.g., `sr=1`, `sq>=0.8`, `sc>=0.7`).
Stage 3: Categorical Balancing mitigates long-tail bias by pruning sparse categories and ensuring a smoother data distribution, resulting in 88K samples for QI and 12K for MA stages.
Enterprise Process Flow: OmniVideo-R1 Pipeline
Data Preparation
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Query Intention (QI) Stage
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Modality Attention (MA) Stage
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Reinforced Multimodal Reasoning
+21.1%
Improvement on OmniVideoBench over Qwen3-Omni-30B-A3B
| Feature/Model | Qwen3-Omni-30B-A3B (Thinking) | Video-SALMONN 2+-72B | OmniVideo-R1 |
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| OmniVideoBench Accuracy | 37.0% | 57.8% (Estimated from overall SOTA) | 44.8% |
| Daily-Omni Accuracy | 75.8% | 79.4% | 82.8% |
| IntentBench Accuracy | 68.5% | 57.3% | 74.2% |
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Case Study: Leveraging Audio-Visual Cues (Figure 5)
In Figure 5, the model is asked to identify the action of a man in black after a query. The QI-only stage (top) initially fails, focusing on visual fleeing and ignoring the audio cue "come down". It provides an incorrect answer 'C. He ignored me and ran away.'.
In contrast, the QI+MA stage (bottom) correctly integrates the audio cue "The speaker asks the man in black to come down" with the visual "man in black jumps down and rolls on the ground". This leads to the correct answer 'D. He jumped down and rolled.', demonstrating OmniVideo-R1's ability to synergistically fuse information and overcome unimodal shortcuts for accurate reasoning.
Insight: This highlights how Modality-Attentive Fusion ensures critical audio cues are not overlooked, enhancing overall reasoning robustness and leading to accurate multimodal understanding.
Calculate Your AI Advantage
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Estimated Annual Savings
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Annual Hours Reclaimed
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Your AI Implementation Roadmap
Our structured approach ensures a seamless integration of OmniVideo-R1 into your existing enterprise architecture, maximizing impact with minimal disruption.
01. Strategic Assessment & Customization
Detailed analysis of current systems, data, and business objectives. Customization of OmniVideo-R1 for specific enterprise needs and workflows.
02. Data Integration & Model Training
Secure integration with enterprise data sources and fine-tuning OmniVideo-R1 on proprietary datasets for optimal performance.
03. Pilot Deployment & Iteration
Controlled pilot deployment in a key business unit, gathering feedback, and iterative model refinement for peak efficiency.
04. Full-Scale Rollout & Support
Comprehensive deployment across the enterprise with continuous monitoring, performance optimization, and dedicated support.
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