Multimodal Relevance Prediction via Dense Embedding Sub-networks
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Solution Overview
Problem
Existing neural network architectures face challenges in predicting the relevance of multimodal content, such as ensuring compatibility and meaningful contribution of features from different modalities, managing high-dimensional data, and achieving efficient and accurate predictions in real-time scenarios.
Innovation Solution
The proposed solution involves a neural network architecture that uses separate dense sub-networks for reducing the dimensionality of image and text embeddings, followed by late fusion with additional feature embeddings, and then inputting the fused embeddings into a third dense sub-network for prediction determination.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If separate dense sub-networks are used for reducing dimensionality of image and text embeddings, then computational efficiency is improved, but device complexity increases
Solution Approach 1:
The neural network is divided into separate dense sub-networks, each responsible for processing specific modalities (image embeddings, text embeddings, additional features). This segmentation allows independent dimensionality reduction for each modality, improving computational efficiency while maintaining manageable complexity through modular design
Solution Approach 2:
The patent applies dimensionality reduction by transforming high-dimensional embeddings into lower-dimensional representations through dense sub-networks. This changes the dimensionality of input data, reducing computational burden while preserving essential features for relevance prediction
2Adaptability or versatility
If late fusion of embeddings is performed, then adaptability of feature integration is improved, but loss of information increases
Solution Approach 1:
Dimensionality reduction is performed preliminarily on each embedding type before fusion. This preliminary processing prepares the data for efficient integration while maintaining the essential characteristics of each modality, enabling adaptive late fusion without excessive information loss
Solution Approach 2:
Each dense sub-network is specialized for processing specific types of embeddings (image, text, additional features), applying local quality principles where different parts of the network have different functions optimized for their specific input types
3Measurement precision
If independent fine-tuning of modalities is enabled, then measurement precision of modality relevance is improved, but device complexity increases
Solution Approach 1:
The network is segmented into independent sub-networks for each modality, allowing separate fine-tuning of each component. This segmentation enables precise adjustment of each modality's contribution to the final prediction while maintaining overall system coherence through the fusion layer
Solution Approach 2:
The architecture supports dynamic fine-tuning where each dense sub-network can be independently adjusted and optimized. This dynamic capability allows the system to adapt to different data distributions and improve measurement precision for modality relevance through targeted training adjustments
Data Source
AI summary
Computer-implemented techniques for multimodal content relevance prediction using neural networks involves processing multimodal content comprising a digital image and text. Initially, dense embeddings are obtained: an image embedding from a pretrained convolutional neural network, and a text embedding from a pretrained transformer network. These embeddings encapsulate the features of the image and text respectively. Two pretrained dense neural sub-networks then reduce the dimensionality of these embeddings. A third dense neural sub-network determines a numerical score for the multimodal content using the reduced embeddings and an additional feature embedding. This score reflects various aspects of the multimodal content, leading to an action taken based on this numerical evaluation, providing a comprehensive and nuanced understanding and management of multimodal digital content.