Cross-Modality Semantic Model Training for Text Image Fusion
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Solution Overview
Problem
Current multimodal processing methods fail to capture sufficient semantic information during model training, particularly in the relationship between text and vision modalities, resulting in poor training and recognition effects.
Innovation Solution
A cross-modality processing method that combines corpus and image data to generate training samples, trains a semantic model to learn semantic vectors containing combinations of both, and applies this model for cross-modality processing, enabling improved semantic relation learning and recognition between text and images.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If current multimodal processing methods are used, then processing speed is maintained, but semantic information capture is insufficient and training effect deteriorates
Solution Approach 1:
The patent merges text and image modalities into a unified multimodal processing framework. The system combines text features and image features through feature fusion layers, creating integrated representations that capture semantic relationships between different modalities. This merging enables the model to simultaneously process and understand both text and image information, resolving the contradiction by ensuring comprehensive semantic information capture while maintaining reliable training effects through unified loss functions and joint optimization
2Reliability
If semantic relations between text and vision modalities are not established, then model complexity is reduced, but recognition capability deteriorates
Solution Approach 1:
The patent introduces semantic relation modules as intermediary components that bridge text and vision modalities. These modules include attention mechanisms and relation extraction layers that explicitly model the semantic connections between text elements and image regions. The intermediaries transform complex cross-modal relationships into structured representations, improving recognition capability while managing model complexity through modular architecture and selective feature interaction
3Loss of information
If comprehensive multimodal training is implemented, then semantic understanding is improved, but training time increases
Solution Approach 1:
The patent implements preliminary action through pre-training strategies and feature extraction optimizations. The system pre-extracts features from large datasets using efficient encoders, pre-computes attention matrices, and uses progressive training approaches where simpler modalities are trained first before introducing complex cross-modal interactions. This preliminary preparation reduces the computational burden during full multimodal training, enabling comprehensive semantic understanding while controlling training time through efficient data loading, caching, and parallel processing techniques
Data Source
AI summary
A cross-modality processing method is related to a field of natural language processing technologies. The method includes: obtaining a sample set, wherein the sample set includes a plurality of corpus and a plurality of images; generating a plurality of training samples according to the sample set, in which each of the plurality of the training samples is a combination of at least one of the plurality of the corpus and at least one of the plurality of the images corresponding to the at least one of the plurality of the corpus; adopting the plurality of the training samples to train a semantic model, so that the semantic model learns semantic vectors containing combinations of the corpus and the images.