Do Modern Video-LLMs Need to Listen? A Benchmark Audit and Scalable Remedy
Do Modern Video-LLMs Need to Listen? By integrating audio encoders, LLaVA-OneVision enhances cross-modal reasoning.
Key Findings
Methodology
The study audits 10 video benchmarks, revealing most tasks solvable via visual cues. By attaching an audio encoder and comparing five compression architectures, the study achieves effective integration of audio and visual information under 25-fold compression.
Key Results
- Audio encoders significantly improve tasks requiring speech comprehension or cross-modal grounding, such as a 1.4 percentage point increase in AVQA benchmark.
- In long videos, audio contributes more significantly, especially when visual cues are sparse.
- Single-frame filtering confirms audio produces significant performance gains in five benchmarks.
Significance
The study reveals the potential of audio encoders in video understanding, challenging the visual dominance of traditional benchmarks and promoting audio application in video comprehension.
Technical Contribution
Proposes a causal Mamba-based design that reduces audio token rates by 25×, enabling scalable audio-visual inference for long-form videos.
Novelty
First systematic comparison of audio token compression architectures, proving that vision token compression techniques effectively transfer to audio.
Limitations
- Existing benchmarks largely ignore audio, underutilizing the potential of audio encoders.
- Integrating audio encoders increases computational complexity.
- Further optimization is needed for audio-visual integration.
Future Work
Future research could develop more efficient audio compression algorithms and design more challenging audio-visual benchmarks.
AI Executive Summary
Recent research in video understanding has focused primarily on visual information, neglecting the potential of audio encoders. By auditing 10 existing video benchmarks, the study finds that most tasks can be solved using visual cues alone, leaving audio information underutilized. The study proposes an integration scheme based on LLaVA-OneVision, comparing five compression architectures to achieve effective integration of audio and visual information. In multiple benchmarks, audio encoders significantly improve tasks requiring speech comprehension or cross-modal grounding. The study reveals the potential of audio encoders in video understanding, challenging the visual dominance of traditional benchmarks and promoting audio application in video comprehension. Future research could develop more efficient audio compression algorithms and design more challenging audio-visual benchmarks.
Deep Analysis
Background
In the evolution of video understanding, visual information has been the primary focus. However, with the emergence of audio encoders like Whisper, which excel in speech recognition, speaker analysis, and sound event detection, the potential of audio information is becoming evident. Existing benchmarks like ActivityNetQA and NExTQA mainly focus on visual recognition and temporal structure, neglecting audio potential.
Core Problem
Most existing video understanding benchmarks do not require audio information, limiting the potential of audio encoders. This restricts the performance of video LLMs in tasks requiring speech comprehension or cross-modal grounding.
Innovation
The study innovatively audits 10 video benchmarks, revealing issues of visual dominance. By attaching audio encoders and comparing five compression architectures, the study achieves effective integration of audio and visual information.
Methodology
- �� Audit 10 video benchmarks, analyzing the contribution of visual and audio information.
- �� Attach audio encoders, comparing five compression architectures under 25-fold compression.
- �� Use single-frame filtering to verify the contribution of audio information.
Experiments
The experimental design includes 10 video benchmarks, using single-frame filtering to remove visually solvable items. Five compression architectures are compared across different benchmarks to analyze the contribution of audio encoders.
Results
Audio encoders significantly improve tasks requiring speech comprehension or cross-modal grounding, such as a 1.4 percentage point increase in AVQA benchmark. In long videos, audio contributes more significantly, especially when visual cues are sparse.
Applications
The integration of audio encoders can be applied to tasks requiring speech comprehension or cross-modal grounding, such as meeting records and lecture summaries. Its performance in long videos is particularly notable.
Limitations & Outlook
Existing benchmarks largely ignore audio, underutilizing the potential of audio encoders. Integrating audio encoders increases computational complexity, and further optimization is needed for audio-visual integration.
Plain Language Accessible to non-experts
Imagine you're in a kitchen preparing dinner. Visual information is like the ingredients and tools you see, while audio information is the sounds you hear while cooking. Traditional video understanding is like a chef who only focuses on visual information, seeing the ingredients but not hearing the cooking sounds. This study is like a chef who can hear the sounds, not just see the ingredients, allowing for a better understanding of the entire cooking process.
ELI14 Explained like you're 14
Imagine you're playing a game where characters are talking. Traditional video understanding is like a player who only sees the characters' actions, while this study is like a player who hears the characters' dialogues. By hearing the dialogues, you can better understand the game's story and make smarter decisions. Isn't that cool? That's the magic of audio encoders!
Glossary
Audio Encoder
A system that converts audio signals into a digital format for processing.
Used to enhance speech and sound event detection in video understanding.
Single-frame Filtering
Testing the contribution of visual information by using only the central frame of a video.
Used to audit the visual dominance in benchmarks.
Cross-modal Reasoning
The process of reasoning by combining multiple sensory information.
Integration of audio and visual information enhances cross-modal reasoning capabilities.
Causal Mamba
A design for audio token compression that reduces token rates.
Supports scalable audio-visual inference for long-form videos.
Benchmark
A standardized test set used to evaluate model performance.
The study audits 10 video benchmarks to analyze the contribution of visual and audio information.
Open Questions Unanswered questions from this research
- 1 Existing benchmarks fail to fully utilize audio information, limiting the potential of audio encoders.
- 2 How to design more challenging audio-visual benchmarks to fully leverage the advantages of audio encoders?
Applications
Immediate Applications
Meeting Records
Enhance the accuracy and efficiency of meeting records using audio encoders, helping users better understand meeting content.
Long-term Vision
Intelligent Video Analysis
Combine audio and visual information to achieve more intelligent video analysis systems, advancing video understanding technology.
Abstract
Speech and audio encoders developed over years of community effort are routinely excluded from video understanding pipelines, not because they fail, but because benchmarks never required listening. We audit 10 video benchmarks and find items largely solvable from visual cues alone: a single-frame probe answers about 76% of AVQA without audio, suggesting poor measurement of audio-visual reasoning. Building on LLaVA-OneVision, we attach a speech/audio encoder and compare five compressor architectures under 25-fold token reduction (25 Hz to 1 Hz). Across 10 benchmarks, with and without filtering, audio yields clear gains on tasks requiring speech comprehension or cross-modal grounding, while vision-centric suites remain largely unaffected. Our results show that speech encoders play a larger role in video understanding than current benchmarks suggest. We will open-source our work at https://github.com/naver-ai/unimambamia-av.