Cross-Modal Retrieval Graph Neural Network Ranking
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Solution Overview
Problem
Current cross-modal retrieval methods face challenges in accurately ranking non-textual content due to the semantic gap between different content types, reliance on noisy and expensive training data, sensitivity to label noise, and inability to model complex relevance patterns, leading to suboptimal search results.
Innovation Solution
A differentiable cross-modal model neural network architecture that combines encoder layers, linear projection layers, and graph neural networks to learn and propagate features across media objects, using edge weights to modulate influence and adapt to different queries, thereby improving ranking accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If standard text-based indexing and retrieval is used for cross-modal retrieval, then the semantic gap between different content types is avoided, but the retrieval accuracy deteriorates due to inability to utilize non-textual features
Solution Approach 1:
The patent introduces a graph neural network as an intermediary structure that connects textual and non-textual features through nodes and edges. The graph structure serves as a mediator that integrates multiple feature types (textual, visual, audio) while maintaining their individual characteristics, allowing the system to utilize non-textual features without directly confronting the semantic gap through simple fusion
2Measurement precision
If paired (image, text) training data is collected to train cross-modal retrieval models, then the model can learn joint representations, but the data collection process becomes too expensive and time-consuming
Solution Approach 1:
The patent extracts and utilizes existing textual content from multimedia documents (captions, titles, descriptions) that already accompany images, videos, and audio files. Instead of requiring new paired data collection, the system extracts available text and integrates it with non-textual features through the graph neural network, significantly reducing data preparation time and cost
3Ease of manufacture
If query logs are used for training, then data collection is simplified, but the intent gap between queries and actual content becomes too large, reducing retrieval effectiveness
Solution Approach 1:
The patent implements dynamic feature weighting and adaptive graph construction that adjusts to different query characteristics. The graph neural network dynamically adjusts node and edge weights based on the specific query context, allowing the system to adapt to varying query intents and reduce the intent gap even when using query log data
4Adaptability or versatility
If multi-graph propagation technique is used for video search re-ranking, then multimodal fusion is achieved, but the model fails to learn the final ranking and object representations
Solution Approach 1:
The patent merges the strengths of multi-graph propagation with learning-to-rank neural networks. The graph neural network maintains the multimodal fusion capability of propagation techniques while adding differentiable ranking layers that learn optimal ranking functions. The combination allows both effective multimodal integration and accurate ranking prediction through end-to-end training
5Adaptability or versatility
If unsupervised cross-modal pseudo-relevance feedback is used, then the system can operate without labeled data, but the approach becomes sensitive to label noise and cannot model complex relevance patterns
Solution Approach 1:
The patent implements a feedback mechanism where the graph neural network iteratively refines relevance scores by propagating information through the graph structure. The model uses feedback from multiple modalities and graph connections to correct errors and reduce sensitivity to noise, while still operating in an unsupervised manner for new queries
Data Source
AI summary
A system and method perform cross-modal information retrieval, by generating a graph representing the set of media objects. Each node of the graph corresponds to a media object and is labeled with a set of features corresponding to a text part of the respective media object. Each edge between two nodes represents a similarity between a media part of the two nodes. A first relevance score is computed for each media object of the set of media objects that corresponds to a text-based score. A second relevance score is computed for each media object by inputting the graph into a graph neural network. The first relevance score and the second relevance score are combined to obtain a final ranking score for each media object.