Don't Let the Video Speak: Audio-Contrastive Preference Optimization for Audio-Visual Language Models
Proposed ACPO method significantly reduces audio hallucination in audio-visual language models.
Key Findings
Methodology
ACPO is a dual-axis preference learning framework that penalizes visual descriptions masquerading as audio facts through an output-contrastive objective and penalizes generation invariant to true auditory signals via an input-contrastive objective.
Key Results
- ACPO improved audio hallucination detection accuracy on the AVHBench dataset to 79.9%, significantly outperforming baseline models.
- On the CMM dataset's audio-language task, ACPO achieved 81.3% accuracy, demonstrating better understanding of audio signals.
- Ablation studies confirmed ACPO's consistent improvements across different architectures.
Significance
This research provides more reliable audio grounding for audio-visual language models, addressing the long-standing issue of visual dominance and enhancing model trustworthiness in real-world applications.
Technical Contribution
ACPO introduces contrastive preference optimization, overcoming the limitation of existing methods that focus only on single modality, offering new theoretical guarantees and engineering possibilities.
Novelty
ACPO is the first to introduce dual-axis contrastive learning in audio-visual language models, significantly reducing video-driven audio hallucination with fundamental innovation over existing methods.
Limitations
- ACPO may perform poorly in extreme audio-visual mismatches as models may still rely on visual cues.
- Requires a large amount of annotated data for training, increasing data preparation complexity.
Future Work
Future work could explore ACPO's application in more multimodal tasks and investigate more efficient data annotation methods to reduce training costs.
AI Executive Summary
In recent years, audio-visual language models (AVLMs) have made significant progress in multimodal tasks, but their reliability is limited by cross-modal hallucination. Specifically, the issue of video-driven audio hallucination arises when models rely on visual shortcuts to generate expected sounds, ignoring true auditory evidence. To address this, researchers propose the Audio-Contrastive Preference Optimization (ACPO) method. This method significantly reduces the occurrence of visual descriptions masquerading as audio facts by introducing output-contrastive and input-contrastive objectives.
Experimental results demonstrate that ACPO performs excellently across multiple benchmark datasets, improving audio hallucination detection accuracy and showing consistent improvements across different architectures. This method not only enhances the understanding of audio signals but also achieves breakthroughs in the trustworthiness of audio-visual language models.
Despite the significant progress made by ACPO in reducing audio hallucination, challenges remain in handling extreme audio-visual mismatches. Future research could explore more efficient data annotation methods to reduce training costs and apply ACPO to more multimodal tasks.
Deep Analysis
Background
Audio-visual language models (AVLMs) extend vision-language models (VLMs) by processing text, video, and audio simultaneously. This field has made significant progress recently, especially in practical applications such as autonomous driving and assistive technologies. However, the reliability of AVLMs is limited by cross-modal hallucination, particularly the issue of video-driven audio hallucination.
Core Problem
AVLMs often rely on visual cues to generate audio descriptions, leading to audio hallucination. This phenomenon severely affects the model's trustworthiness, as the generated content may not match the true audio.
Innovation
ACPO introduces a dual-axis contrastive learning framework that significantly reduces video-driven audio hallucination. Its innovation lies in penalizing visual descriptions masquerading as audio facts and generation invariant to true auditory signals.
Methodology
- �� Output-contrastive objective: Penalizes visually-driven audio descriptions by swapping audio tracks.
- �� Input-contrastive objective: Evaluates text outputs across different audio tracks, penalizing generation invariant to audio signals.
- �� Lightweight framework: Fine-tunes only the audio projection layer to enhance audio grounding.
Experiments
Experiments were conducted on AVHBench and CMM datasets, comparing with various baseline models. Key hyperparameters include learning rate and batch size. Ablation studies confirmed ACPO's effectiveness across different architectures.
Results
ACPO improved audio hallucination detection accuracy on the AVHBench dataset to 79.9%. On the CMM dataset's audio-language task, ACPO achieved 81.3% accuracy, demonstrating better understanding of audio signals.
Applications
ACPO can enhance audio signal processing capabilities in autonomous driving and assistive technologies, requiring high-quality annotated data to ensure training effectiveness.
Limitations & Outlook
ACPO may perform poorly in extreme audio-visual mismatches and requires a large amount of annotated data for training. Future research could explore more efficient data annotation methods.
Plain Language Accessible to non-experts
Imagine you're in a kitchen where vision is the ingredients you see, and audio is the cooking sounds you hear. Usually, you might guess what's being cooked based on the ingredients you see, ignoring the actual cooking sounds. This is like visual-driven audio hallucination. The ACPO method is like a smart chef who can judge what's being cooked based on the actual cooking sounds, not just the ingredients seen.
ELI14 Explained like you're 14
Imagine you're playing a game with lots of different sounds and visuals. Usually, you might guess the game's sounds based on the visuals, but sometimes they don't match. ACPO is like a super-smart gamer who can judge the game's situation based on the actual sounds, not being fooled by the visuals. This way, you can understand the game's storyline more accurately!
Glossary
Audio-Visual Language Model (AVLM)
A model capable of processing text, video, and audio simultaneously, extending the capabilities of vision-language models.
Used in multimodal tasks like autonomous driving and assistive technologies.
Cross-Modal Hallucination
When a model generates content that doesn't match the true input, often due to reliance on incorrect modality cues.
In AVLMs, particularly video-driven audio hallucination.
Audio-Contrastive Preference Optimization (ACPO)
A dual-axis contrastive learning framework aimed at reducing audio hallucination.
Enhances audio grounding by penalizing visual descriptions masquerading as audio facts.
Output-Contrastive Objective
Penalizes visually-driven audio descriptions by swapping audio tracks.
A key step in the ACPO method.
Input-Contrastive Objective
Evaluates text outputs across different audio tracks, penalizing generation invariant to audio signals.
A key step in the ACPO method.
Open Questions Unanswered questions from this research
- 1 How to improve ACPO's performance in extreme audio-visual mismatches? Current methods perform poorly in such scenarios.
- 2 How to reduce ACPO's reliance on large amounts of annotated data? This would help lower training costs.
Applications
Immediate Applications
Autonomous Driving
Enhances vehicle understanding of environmental audio signals, reducing visual-driven misjudgments.
Long-term Vision
Smart Assistants
Improves response accuracy in multimodal environments, enhancing user experience.
Abstract
While Audio-Visual Language Models (AVLMs) have achieved remarkable progress over recent years, their reliability is bottlenecked by cross-modal hallucination. A particularly pervasive manifestation is video-driven audio hallucination: models routinely exploit visual shortcuts to hallucinate expected sounds, discarding true auditory evidence. To counteract this deeply ingrained visual dominance, we propose Audio-Contrastive Preference Optimization (ACPO). This dual-axis preference learning framework introduces an output-contrastive objective to penalize visual descriptions masquerading as audio facts, alongside an input-contrastive objective that swaps audio tracks to explicitly penalize generation invariant to the true auditory signal. Extensive experiments demonstrate that ACPO establishes highly faithful audio grounding and mitigates audio hallucination.