Attentive Graph Networks for Fine-Grained Object Localization
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Solution Overview
Problem
Existing referring expression comprehension models struggle to localize a target object from an image set with similar visual elements due to their inability to handle compositional expressions and subtle visual differences effectively.
Innovation Solution
The method involves decomposing a fine-grained referring expression into different modules, using language-guided graph neural networks (GNNs) to extract visual regional proposals and aggregate matching similarities, enabling the localization of target objects in images with similar visual elements.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If existing referring expression comprehension models are used, then the system can process simple referring expressions, but it fails to handle fine-grained relationships and subtle visual differences in images with similar visual elements
Solution Approach 1:
The patent segments the referring expression into multiple textual modules (e.g., noun phrase, adjective phrase, prepositional phrase) and processes each module separately through dedicated neural network modules. This segmentation enables the system to capture fine-grained relationships by analyzing each linguistic component independently and then integrating the results, thereby improving localization accuracy for complex expressions with similar visual elements.
2Adaptability or versatility
If the model processes compositional text expressions, then it can handle more complex referring expressions, but it loses ability to distinguish subtle visual differences
Solution Approach 1:
The patent introduces a graph neural network dimension that models relationships between visual regions as a graph structure, where nodes represent regions and edges represent relationships. This dimensional transformation allows the system to process compositional text expressions while simultaneously capturing fine-grained visual relationships, as the graph structure preserves spatial and relational information that would otherwise be lost in flat processing.
3Device complexity
If the system uses traditional neural networks, then the architecture is simpler, but it cannot effectively mine fine-grained object relations from visual regional proposals
Solution Approach 1:
The patent introduces graph neural networks as an intermediary between the visual region proposal module and the textual module matching. The GNN acts as a mediator that processes visual regional proposals and extracts fine-grained object relations before passing them to the matching stage. This intermediary component enables the system to preserve and utilize fine-grained relational information without requiring complete architectural redesign.
4Productivity
If the model aggregates all textual modules together, then processing is more efficient, but it cannot capture individual module contributions to matching similarity
Solution Approach 1:
The patent segments the matching process into individual textual module evaluations, where each textual module is matched against visual regions separately through dedicated neural network modules. The matching similarities from different textual modules are then aggregated to produce the final result. This segmented approach maintains processing efficiency while preserving module-level matching information, as each module's contribution is independently calculated and then combined.
Data Source
AI summary
A computer-implemented method for fine-grained referring expression comprehension is provided. The computer-implemented method includes receiving, at a processor, a textual expression and an image as inputs and executing, at the processor, fine-grained referring expression comprehension. The executing includes decomposing the textual expression into different textual modules, extracting visual regional proposals from the image, using language-guided graph neural networks to mine fine-grained object relations from the visual regional proposals and aggregating different matching similarities between the different textual modules and the fine-grained object relations.