Cross-Modal Graph Attention for Text-Image Object Matching
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Solution Overview
Problem
Existing neural network architectures struggle to accurately match textual expressions with target objects in images due to a gap between visual and textual information, and the output of object detection networks often exceeds the number of entities in the language expression, leading to irrelevant objects influencing the target.
Innovation Solution
A recognition method using a text recognition network to generate entity and relation features, combined with an object detection network to generate candidate regions, and an enhanced cross-modal graph attention network to generate node and compound features, which are then matched to calculate first and second scores for enhanced accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If object detection network generates multiple candidate regions, then the coverage of potential target objects is improved, but the number of irrelevant objects affecting detection accuracy increases
Solution Approach 1:
The patent segments the matching process into two distinct stages: (1) entity-level matching between text entities and candidate regions to generate first scores, and (2) overall text-to-compound feature matching to generate second scores. This segmentation allows the system to handle multiple candidate regions effectively by evaluating them at different granularity levels, thereby maintaining detection accuracy despite the presence of irrelevant objects.
Solution Approach 2:
The patent introduces an intermediary scoring mechanism that acts as a mediator between the object detection network output and the final target identification. By computing both first scores (entity-level) and second scores (overall-level) and combining them, the system creates an intermediate evaluation layer that filters out irrelevant objects while preserving potential targets, thus resolving the contradiction between coverage and accuracy.
2Measurement precision
If traditional matching methods are used between text and image, then the system complexity is low, but the matching accuracy between textual expressions and target objects is insufficient
Solution Approach 1:
The patent transitions from traditional single-dimensional text-to-object matching to a multi-dimensional matching framework. It introduces entity-level features and overall text features as different dimensions, computing matches in both dimensions and combining the results. This dimensional expansion significantly improves matching accuracy by capturing both local entity correspondences and global text meaning, despite increasing system complexity.
Solution Approach 2:
The patent employs a composite matching approach that combines multiple types of features (entity features, relation features, overall features) and multiple scoring mechanisms (first scores, second scores) to create a composite evaluation system. This composite structure integrates diverse information sources to achieve superior matching accuracy, accepting the necessary increase in system complexity as a trade-off.
Data Source
AI summary
A recognition method includes the following steps. A text is analyzed by a language recognition network to generate an entity feature, a relation feature and an overall feature. An input image is analyzed by an object detection network to generate candidate regions. Node features, aggregated edge features and compound features are generated by an enhanced cross-modal graph attention network according to the entity feature, the relation feature, the candidate regions and the overall feature. The entity feature and the relation feature are matched to the node features and the aggregated edge features to generate the first scores. The overall feature is matched to the compound features to generate second scores. Final scores corresponding to the candidate regions are generated according to the first scores and the second scores.