Method for complementing modal missing data of multi-modal large model

By using feature distribution ellipse correction and global context retrieval, the problem of modality missing in multimodal data processing is solved, generating high-quality structured feature representations and improving the accuracy and coherence of multimodal data completion.

CN121350435AActive Publication Date: 2026-01-16ZHONGSHU (XIAMEN) INFORMATION TECH CO LTD
5 Cites 1 Cited by

Patent Information

Application Number
CN202511913401.0
Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
Filing Date
2025-12-18
Publication Date
2026-01-16
Estimated Expiration
2045-12-18

AI Technical Summary

Technical Problem

Existing multimodal data processing methods fail to fully consider the structural correlation of feature distribution when modalities are missing, resulting in limited modeling capabilities for complex contextual dependencies and affecting the semantic coherence of multimodal data and the accuracy of completion results.

Method used

By using feature distribution ellipse correction and global context retrieval, the accuracy and intelligence of multimodal missing data completion are improved. This includes missing value detection, feature reconstruction and calibration, generation of structured feature representation, analysis of intramodal and intermodal contextual dependencies, and retrieval of relevant contextual patterns from a global multimodal pattern library for completion.

Benefits of technology

It improves the accuracy and intelligence of multimodal missing data completion, reduces interference caused by missing data, and enhances the rationality and consistency of the completion results.

✦ Generated by Eureka AI based on patent content.
Patent Text Reader

Abstract

The invention provides a multi-modal large model modal missing data complementing method, and relates to the technical field of data processing, the method comprises the following steps: 1, carrying out missing value detection and coding processing on multi-modal input data, analyzing the correlation between internal characteristics of the data, generating structured characteristic representation through characteristic recombination, and carrying out data processing on the structured characteristic representation; generating a feature calibration parameter by combining the correlation between the local features, and obtaining a numeric feature vector and a mask matrix of each mode; step 2, inputting the numeric feature vector and the mask matrix into a feature fusion model, analyzing a context dependency relationship in a mode and between modes, and performing rotation correction on an ellipse feature by calculating a principal axis parameter of a feature distribution ellipse to generate a preliminarily corrected fusion feature representation; according to the method, through feature distribution ellipse correction and global context retrieval, the accuracy and intelligence of multi-modal missing data complementation are improved.
Need to check novelty before this filing date? Find Prior Art