Partitioned Attention Neural Network for Multimodal Processing
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Solution Overview
Problem
Existing attention-based neural networks struggle with multimodal processing as the representations become fully entangled, leading to issues with independent feature extraction and handling missing modalities during inference.
Innovation Solution
The system employs techniques to control input routing within attention-based neural networks, ensuring modality-specific internal representations by updating latent vectors using attention only within each modality, while also generating fused latent vectors from all modalities.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If inputs from multiple modalities are concatenated and fed to a single backbone network, then the processing is simple and requires little fusion engineering, but the representations become fully entangled and independent features for each modality are lost
Solution Approach 1:
The patent divides the network into separate backbone networks for each modality (image backbone, video backbone, audio backbone) instead of using a single shared backbone. This segmentation allows each modality to maintain its own independent feature representations while still enabling multi-modal processing, thus resolving the contradiction between processing simplicity and feature independence.
2Productivity
If representations are fully entangled across modalities, then the model can leverage all available information, but contrastive learning cannot operate effectively because independent features for each modality are required
Solution Approach 1:
By segmenting the network into separate backbone networks for each modality, the patent enables contrastive learning to operate effectively on each modality's independent features while still allowing the model to leverage all available information through the task-specific head that processes all modalities together.
3Productivity
If representations are fully entangled, then the model processes all modalities together, but the model cannot effectively perform uni-modal tasks or process inputs when one modality is missing
Solution Approach 1:
The patent segments the processing into separate backbone networks for each modality, allowing the model to process each modality independently when needed. The task-specific head can then combine features from available modalities, enabling effective uni-modal task performance and robustness to missing modalities while maintaining multi-modal processing capability.
4Device complexity
If a single backbone network is used for all modalities, then the architecture is simple, but the model cannot maintain modality-specific internal representations
Solution Approach 1:
The patent uses separate backbone networks for each modality to preserve modality-specific representations, while keeping the overall architecture relatively simple through a shared task-specific head that processes features from all modalities. This segmentation approach maintains representational integrity without excessive complexity.
Data Source
AI summary
Methods, systems, and apparatus, including computer programs encoded on computer storage media, for processing network inputs using a neural network that implements partitioned attention.