Multimodal Transformer Distillation for Audio-Visual Synchronization (2210.15563v3)

Abstract: Audio-visual synchronization aims to determine whether the mouth movements and speech in the video are synchronized. VocaLiST reaches state-of-the-art performance by incorporating multimodal Transformers to model audio-visual interact information. However, it requires high computing resources, making it impractical for real-world applications. This paper proposed an MTDVocaLiST model, which is trained by our proposed multimodal Transformer distillation (MTD) loss. MTD loss enables MTDVocaLiST model to deeply mimic the cross-attention distribution and value-relation in the Transformer of VocaLiST. Additionally, we harness uncertainty weighting to fully exploit the interaction information across all layers. Our proposed method is effective in two aspects: From the distillation method perspective, MTD loss outperforms other strong distillation baselines. From the distilled model's performance perspective: 1) MTDVocaLiST outperforms similar-size SOTA models, SyncNet, and Perfect Match models by 15.65% and 3.35%; 2) MTDVocaLiST reduces the model size of VocaLiST by 83.52%, yet still maintaining similar performance.

• The paper presents MTDVocaLiST, which uses multimodal transformer distillation to mimic teacher model behaviors for improved audio-visual synchronization.
• It leverages cross-attention distributions and uncertainty weighting to reduce model size by 83.52% while outperforming similar-size models by up to 15.65%.
• The approach enables real-time multimodal processing on mobile and edge devices, bridging sophisticated theory with practical deployment in multimedia applications.

Multimodal Transformer Distillation for Audio-Visual Synchronization

The paper "Multimodal Transformer Distillation for Audio-Visual Synchronization" introduces a novel approach to the task of determining synchronization between audio and visual components in videos, with a specific focus on the alignment of speech and mouth movements. This task is increasingly pertinent, particularly within multimedia applications that require real-time processing, such as video conferencing and multimedia streaming, where resources might be limited.

Proposed Model: MTDVocaLiST

The authors propose a model called MTDVocaLiST, which leverages a newly introduced Multimodal Transformer Distillation (MTD) loss that effectively mimics the behavior of large, resource-intensive models. The design of MTDVocaLiST stems from the need to create a model that maintains high accuracy while being lightweight enough for practical deployment on devices with limited computational resources. This is important for mobile or edge devices where trade-offs between performance and model size are critical.

Methodology

The MTDVocaLiST model is built on the success of the VocaLiST framework but introduces a distillation process meant to capture and emulate critical model behaviors in a reduced size. The multimodal interaction knowledge from a state-of-the-art model (VocaLiST) is distilled into a smaller, more efficient model. This distillation process specifically involves learning cross-attention distributions and value-relation metrics of the teacher model’s Transformer layers. An innovative aspect here is the use of uncertainty weighting, which accommodates the differential significance of Transformer behaviors across layers, thereby enhancing distillation fidelity.

Results

The MTDVocaLiST achieves excellent results, outperforming similar-size state-of-the-art models, such as SyncNet and Perfect Match (PM), by 15.65% and 3.35%, respectively. Additionally, it reduces the model size of VocaLiST by 83.52% while maintaining a comparable performance. These results demonstrate the effectiveness of the multimodal distillation approach, specifically the gains achieved from capturing cross-attention and value-relation behavior from larger models.

Implications

Practically, MTDVocaLiST showcases the feasibility of deploying sophisticated audio-visual synchronization algorithms on resource-constrained devices, opening pathways for extensive applications in mobile computing. Theoretically, the work reaffirms the potential of knowledge distillation, especially in cross-modal tasks. It suggests that for multimodal tasks, consideration of the Transformer behaviors, such as attention and value-relation, is crucial and can be effectively distilled into smaller architectures without a significant loss of performance.

Future Directions

The paper provides a foundation for future work to investigate other multimodal tasks and the potential of different distillation strategies. This research could be extended to explore how similar methodologies can be applied to different data modalities, such as text and image combinations. Furthermore, examining the adaptability of such distilled models in varying real-world conditions, and their robustness against adversarial examples, would contribute valuably to the field’s understanding of practical deployments.

Overall, the introduction of MTDVocaLiST is a significant contribution towards the efficient implementation of multimodal models, with ample scope for applying these principles to other domains in artificial intelligence.