Multiscale Multimodal Transformer for Audio-Video Action Alignment
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Solution Overview
Problem
Existing action recognition models primarily rely on visual cues, limiting their effectiveness in applications where actions are not visually well-defined, and lack efficient integration of audio and video modalities for robust perception.
Innovation Solution
A multiscale multimodal transformer (MMT) model is developed, incorporating a multiscale audio transformer (MAT) and video transformer (MVT) with enhanced multimodal alignment loss functions, audio-video contrastive loss, and intra-modality contrastive loss to align and fuse audio and video features effectively.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If existing action recognition models rely primarily on visual cues, then the model structure remains relatively simple, but the recognition accuracy deteriorates in applications where actions are not visually well-defined
Solution Approach 1:
The patent combines audio and video modalities into a unified multimodal action recognition model. The MMT architecture integrates audio transformer and video transformer branches, fusing their features through cross-modal attention mechanisms to achieve more accurate action recognition than visual-only models.
Solution Approach 2:
The model segments the processing of different modalities into separate specialized components: an audio transformer branch for audio feature extraction and a video transformer branch for visual feature extraction. This segmentation allows each modality to be processed optimally before fusion.
2Reliability
If audio and video modalities are integrated using conventional methods, then the model can process multiple input types, but the feature alignment and fusion effectiveness deteriorates without specialized alignment mechanisms
Solution Approach 1:
The patent implements contrastive loss functions that provide feedback signals during training to align audio and video features. The audio-video contrastive loss computes similarity between corresponding audio and video features, guiding the model to learn consistent representations across modalities through iterative optimization.
Solution Approach 2:
The model introduces bottleneck tokens as intermediary representations that mediate between audio and video modalities. These bottleneck tokens serve as a common representation space where cross-modal alignment can occur, facilitating effective feature fusion while maintaining modality-specific characteristics.
3Measurement precision
If conventional transformers are used for audio and video processing, then the architecture remains simple, but the model fails to capture hierarchical representations effectively
Solution Approach 1:
The patent implements dynamic scaling mechanisms in the transformer architecture that adaptively adjust the capacity and depth of hierarchical feature extraction based on the input characteristics. This allows the model to capture multi-scale temporal and spatial patterns while maintaining computational efficiency.
Solution Approach 2:
The model extends the standard transformer architecture by incorporating additional hierarchical dimensions for processing audio and video signals. This includes multi-scale temporal modeling and spatial hierarchy extraction, adding dimensional complexity to capture hierarchical representations effectively.
Data Source
AI summary
Techniques for implementing a multiscale multimodal transformer for multimodal action recognition with a computer are described. According to some examples, a computer-implemented method includes training a multiscale audio transformer (MAT) machine learning model to extract hierarchical audio representations; and generating an audio inference by the MAT machine learning model for an input audio file.