Multimodal Neural Network Cross-Modal Feature Fusion
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Solution Overview
Problem
Existing multimodal data processing methods struggle to fully utilize the information from different modalities, leading to one-sided understanding and limited interaction between modalities.
Innovation Solution
A neural network architecture is proposed that includes an input subnetwork, cross-modal feature subnetworks, cross-modal fusion subnetworks, and an output subnetwork, which processes multimodal data by calculating cross-modal features and fusing them to enhance interaction and understanding.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If existing multimodal data processing methods are used, then the processing is simpler, but the interaction between modalities is limited and understanding is one-sided
Solution Approach 1:
The neural network is divided into multiple specialized subnetworks: input subnetwork for data reception, cross-modal feature subnetworks for extracting features from different modality pairs, cross-modal fusion subnetworks for integrating features, and output subnetwork for results. Each subnetwork has a specific function, allowing complex multimodal processing to be broken down into manageable segments that can be optimized independently.
Solution Approach 2:
Cross-modal feature subnetworks act as intermediaries between the input subnetwork and cross-modal fusion subnetworks. These intermediate layers extract and transform features from different modalities (video, audio, text) into a standardized representation that can be effectively fused, enabling deep interaction between modalities without direct complex connections between all components.
2Loss of information
If cross-modal feature subnetworks and fusion subnetworks are added to enhance modality interaction, then the understanding of multimodal data deepens, but the device complexity increases
Solution Approach 1:
The cross-modal feature subnetworks and fusion subnetworks are designed with universal structures that can process multiple types of modalities (video, audio, text) through the same architectural framework. The subnetworks use shared computational patterns and parameter sharing strategies, allowing the system to handle diverse modality combinations without requiring completely separate processing paths for each modality pair.
Solution Approach 2:
The neural network employs a nested architecture where cross-modal feature subnetworks are embedded within the broader processing framework, and cross-modal fusion subnetworks are nested within the feature extraction layer. This hierarchical nesting allows information to flow through progressively more integrated processing stages, with each layer building upon the previous one, efficiently utilizing information from all modalities.
Data Source
AI summary
Disclosed are a method for processing multimodal data using a neural network, a device, and a medium, and relates to the field of artificial intelligence and, in particular to multimodal data processing, video classification, and deep learning. The neural network includes: an input subnetwork configured to receive the multimodal data to output respective first features of a plurality of modalities; a plurality of cross-modal feature subnetworks, each of which is configured to receive respective first features of two corresponding modalities to output a cross-modal feature corresponding to the two modalities; a plurality of cross-modal fusion subnetworks, each of which is configured to receive at least one cross-modal feature corresponding to a corresponding target modality and other modalities to output a second feature of the target modality; and an output subnetwork configured to receive respective second features of the plurality of modalities to output a processing result of the multimodal data.