Fork-Merge Decoding: Enhancing Multimodal Understanding in Audio-Visual Large Language Models
Proposed Fork-Merge Decoding enhances AV-LLMs' multimodal understanding without additional training.
Key Findings
Methodology
The study introduces Fork-Merge Decoding (FMD), a method for separating and merging audio and video features during inference. Initially, audio and video inputs are processed separately through early decoder layers (fork phase), followed by merging these hidden states for joint reasoning in the remaining layers (merge phase). This approach requires no additional training or architectural changes.
Key Results
- On the AVHBench dataset, FMD reduces VideoLLaMA2's attention weight on video inputs by 14% and increases it on audio inputs by 7%.
- FMD consistently improves performance across all benchmarks, especially in tasks requiring balanced reasoning, on AVQA and MUSIC-AVQA datasets.
- FMD significantly enhances accuracy in audio-visual matching and captioning tasks without altering model architecture.
Significance
This study significantly enhances AV-LLMs' multimodal understanding by optimizing inference strategies without altering existing model architectures. It addresses modality bias issues in previous models, providing a more robust solution for practical applications of multimodal models.
Technical Contribution
The technical contribution lies in proposing an inference strategy that significantly improves balanced and accurate multimodal understanding without additional training. This offers new insights for optimizing inference stages in multimodal large models.
Novelty
FMD is the first method to address modality bias through inference phase separation and merging, offering a distinct innovation compared to previous methods that rely on architectural changes or additional training.
Limitations
- FMD may still exhibit bias when handling extremely imbalanced modality inputs.
- Inference time may increase in resource-constrained environments.
Future Work
Future work could explore validating FMD's effectiveness on larger datasets and further optimizing its computational efficiency.
AI Executive Summary
In recent years, multimodal large language models have made significant progress in processing audio and video information. However, these models often face modality bias issues when jointly processing multimodal inputs, where the model may overly rely on one modality, affecting overall performance.
To address this issue, researchers have proposed a new method called Fork-Merge Decoding (FMD). This method separates and merges audio and video features during inference to achieve more balanced multimodal understanding. Specifically, FMD first processes audio and video inputs separately through early decoder layers (fork phase) and then merges these hidden states for joint reasoning in the remaining layers (merge phase). This approach requires no additional training or architectural changes.
Experimental results show that FMD consistently improves performance across multiple benchmarks, especially in tasks requiring balanced reasoning. The method not only enhances model accuracy but also offers new insights for optimizing inference stages in multimodal large models. However, FMD still needs further optimization when handling extremely imbalanced modality inputs. Future research could explore validating its effectiveness on larger datasets.
Deep Analysis
Background
In recent years, with the success of large language models in text processing tasks, multimodal large language models (MLLMs) have rapidly developed. These models process different modalities through separate encoders and integrate their outputs in a decoder, achieving advanced cross-modal reasoning. However, current audio-visual large language models (AV-LLMs) often face modality bias issues when jointly processing audio and video inputs, where the model may overly rely on one modality, affecting overall performance.
Core Problem
Current audio-visual large language models often exhibit modality bias when processing multimodal inputs. This bias may cause the model to overly rely on one modality, affecting overall performance. Solving this issue is crucial for enhancing the model's multimodal understanding capabilities.
Innovation
The core innovation of the FMD method lies in its inference phase separation and merging strategy. Initially, audio and video inputs are processed separately through early decoder layers (fork phase), followed by merging these hidden states for joint reasoning in the remaining layers (merge phase). This approach requires no additional training or architectural changes, offering a distinct innovation compared to previous methods that rely on architectural changes or additional training.
Methodology
- �� Fork phase: Audio and video inputs are processed separately through early decoder layers, generating modality-specific hidden representations.
- �� Merge phase: In the remaining decoder layers, these hidden states are merged for joint reasoning.
- �� Attention-guided fusion: Adjust attention weights to balance contributions from different modalities, ensuring balanced multimodal understanding.
Experiments
The experimental design includes validating the effectiveness of the FMD method on AVQA, MUSIC-AVQA, and AVHBench datasets. VideoLLaMA2, video-SALMONN, and Qwen2.5-Omni models are used as baselines to evaluate FMD's performance improvements in audio-visual matching, captioning, and other tasks.
Results
Experimental results show that FMD consistently improves performance across all benchmarks, especially in tasks requiring balanced reasoning. Specifically, FMD reduces VideoLLaMA2's attention weight on video inputs by 14% and increases it on audio inputs by 7% on the AVHBench dataset.
Applications
The FMD method can be directly applied to scenarios requiring balanced processing of audio and video information, such as intelligent surveillance and video analysis. Its feature of requiring no additional training makes it highly practical in resource-limited environments.
Limitations & Outlook
Although FMD performs well across multiple benchmarks, it may still exhibit bias when handling extremely imbalanced modality inputs. Additionally, inference time may increase in resource-constrained environments. Future research could explore validating its effectiveness on larger datasets.
Plain Language Accessible to non-experts
Imagine you're cooking in a kitchen with two ingredients: audio and video. Traditional methods mix these two ingredients directly, but this might make one ingredient's flavor too dominant. The FMD method is like processing these two ingredients separately first, allowing each to bring out its best flavor, and then mixing them together at the end. This way, you can better taste each ingredient's unique flavor while enjoying their perfect combination.
ELI14 Explained like you're 14
Imagine you're playing a game that requires you to listen to sounds and watch visuals. Usually, you might focus more on the visuals and ignore the sounds. The FMD method is like a new feature in the game that lets you experience the sounds and visuals separately first, and then combines them. This way, you can enjoy the game's visuals better and also notice the background music and sound effects. This method makes the game experience richer and more fun!
Glossary
Fork-Merge Decoding
A method for separating and merging audio and video features during inference.
Used to address modality bias issues.
Modality Bias
The model's tendency to overly rely on one modality in multimodal inputs.
Affects the accuracy of multimodal understanding.
AV-LLMs
Large language models capable of processing audio and video inputs.
Used for joint processing of multimodal information.
Attention Weight
The degree of focus the model allocates to different inputs during inference.
Used to adjust contributions from different modalities.
Inference Time
The time required for the model to generate output given an input.
Affects the model's real-time application capability.
Open Questions Unanswered questions from this research
- 1 How to further optimize FMD's performance with extremely imbalanced modality inputs?
- 2 Validate FMD's effectiveness on larger datasets.
- 3 Explore FMD's potential applications in other multimodal tasks.
Applications
Immediate Applications
Intelligent Surveillance
Enhance the balanced processing of audio and video information in surveillance systems using FMD.
Video Analysis
Apply FMD in video analysis tasks to improve the integration of multimodal information.
Long-term Vision
Multimodal Human-Computer Interaction
Enhance multimodal understanding capabilities in human-computer interaction systems using FMD, improving user experience.
Abstract
The goal of this work is to enhance balanced multimodal understanding in audio-visual large language models (AV-LLMs) by addressing modality bias without additional training. In current AV-LLMs, audio and video features are typically processed jointly in the decoder. While this strategy facilitates unified multimodal understanding, it may introduce modality bias, where the model tends to over-rely on one modality due to imbalanced training signals. To mitigate this, we propose Fork-Merge Decoding (FMD), a simple yet effective inference-time strategy that requires no additional training or architectural modifications. FMD first performs modality-specific reasoning by processing audio-only and video-only inputs through the early decoder layers (fork), and then merges the resulting hidden states for joint reasoning in the remaining layers (merge). This separation allows each modality to be emphasized in the early stages while encouraging balanced contributions during integration. We validate our method on three representative AV-LLMs-VideoLLaMA2, video-SALMONN, and Qwen2.5-Omni-using three benchmark datasets. Experimental results show consistent gains in audio, video, and audio-visual reasoning tasks, highlighting the effectiveness of inference-time interventions for robust and efficient multimodal understanding.