Multimodal Encoding with Dynamic Layer Switching for Feature Fusion
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Solution Overview
Problem
Existing deep learning models with fixed structures struggle to perform effectively in complex tasks involving image and text data, limiting their versatility and accuracy in applications such as cross-modal retrieval and visual question answering.
Innovation Solution
The encoding apparatus employs a dynamic Switch-BERT model with learnable layer-by-layer interactions, allowing it to adaptively select inputs at different layers and learn flexible interaction modes between modalities, enhancing multimodal feature fusion and interaction.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a fixed-structure deep learning model is used, then the model can achieve excellent performance in specific tasks, but it affects the effect of model usage for more complex tasks
Solution Approach 1:
The patent applies the dynamics principle by transforming the fixed-structure model into a dynamic model with learnable layer-by-layer interactions. The model can adaptively select inputs at different layers and learn flexible interaction modes between modalities, allowing it to dynamically adjust its structure based on the complexity of the task at hand, thus resolving the contradiction between achieving excellent performance in specific tasks and maintaining versatility for complex tasks
2Ease of manufacture
If deep learning models with fixed structures are used, then implementation is straightforward, but they cannot effectively handle complex multimodal tasks requiring flexible interactions
Solution Approach 1:
The model transitions from a static fixed-structure to a dynamic architecture with learnable interactions between layers. This allows the model to automatically adapt its structure for complex multimodal tasks while maintaining reasonable implementation complexity through standardized neural network operations
Solution Approach 2:
The patent segments the model into multiple encoders for different modalities (image encoder, text encoder, etc.) with learnable interaction mechanisms between them. This segmentation allows each modality to be processed independently while enabling flexible interactions when needed, balancing implementation simplicity with versatility for complex tasks
Data Source
AI summary
A computer-implemented system for encoding includes an encoding layer and at least one joint encoding unit. The encoding layer encodes a received first modal initial feature vector and a received second modal initial feature vector, to generate, respectively, a first modal feature vector and a second modal feature vector, joint encoded by the at least one joint encoding unit, where the at least one joint encoding unit includes an encoding module and a modal input switching module. The modal input switching module processes the first modal feature vector and the second modal feature vector, to obtain, respectively a first modal switching encoding vector and a second modal switching encoding vector. The encoding module processes the first modal switching encoding vector and the second modal switching encoding vector, to generate, respectively a first target modal fusion vector and a second target modal fusion vector.