Multimodal Feature Fusion for Low-Label Classification
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing methods for training classification models on data from multiple modalities, such as images and text, require large amounts of labeled multimedia data, which is costly and time-consuming to obtain, especially for image-based content where manual review is necessary.
Innovation Solution
The method involves merging features from different data types, such as text and image or video, to create a common feature space for training a joint model, allowing for classification of content items with multiple types without the need for extensive human-labeled training data, using early fusion techniques to combine features early in the machine learning pipeline.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If large amounts of labeled multimedia data are used for training, then model classification accuracy is improved, but data acquisition cost and time consumption increase significantly
Solution Approach 1:
The patent segments the training data into different modalities (text data and multimedia data) and processes them separately through different neural network branches. Text data can be obtained in large quantities through automated means, while multimedia data is processed more efficiently. This segmentation allows the system to leverage abundant text data to compensate for limited labeled multimedia data, improving classification accuracy without requiring extensive manual labeling of multimedia content.
Solution Approach 2:
The patent introduces an intermediary mechanism by using text data as a bridge to train the multimedia classification model. The text-based branch and multimedia-based branch are trained separately and then their features are fused. This intermediary approach allows the model to learn from abundant text data and apply that knowledge to multimedia classification, reducing the need for large amounts of manually labeled multimedia training data.
2Measurement precision
If manual review and classification is performed for image and video data, then training data quality is improved, but processing cost and time increase
Solution Approach 1:
The patent divides the data processing pipeline into separate segments for text data and multimedia data, each processed by dedicated neural network branches. This segmentation allows automated text processing to occur in parallel with multimedia processing, eliminating the need for sequential manual review of all data types and significantly improving processing efficiency while maintaining data quality.
Solution Approach 2:
The patent creates a universal feature fusion mechanism that can handle both text-based features and multimedia-based features through the same classification head. This multi-functional approach allows the system to process different data types efficiently without requiring separate manual classification pipelines for each modality, thereby improving productivity while maintaining consistent data quality standards.
3Measurement precision
If feature fusion of multiple data types is implemented, then model performance on multimodal content is improved, but system complexity increases
Solution Approach 1:
The patent employs a segmented architecture where text data and multimedia data are processed by separate neural network branches before feature fusion. This segmentation maintains modularity and simplifies the overall system design, as each branch can be independently trained and optimized. The feature fusion layer then combines these processed features in a straightforward manner, achieving improved multimodal classification performance without excessive complexity.
Solution Approach 2:
The patent introduces an intermediary feature fusion layer that bridges the text-based processing branch and the multimedia-based processing branch. This intermediary mechanism systematically combines features from different modalities through defined operations (such as concatenation or attention mechanisms), improving multimodal classification performance while maintaining clear architectural structure and manageable complexity.
Data Source
AI summary
A method includes receiving first data elements of a first data type and second data elements of a second data type. The first data type is text data and the second data type is at least one of image data or video data. The method also includes identifying first features of each of the first data elements, identifying second features of each of the second data elements, and generating merged features by combining a first feature of the first features of each of the first data elements with a second feature of the second features of one of the second data elements. The first feature and the second feature each represent a common feature. The method also includes generating a model based on the common features and at least a portion of the first features and the second features.