Modularized Graph Attentive Networks for Image Grounding
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Solution Overview
Problem
Existing image grounding technologies struggle to precisely match complex textual expressions with visual properties in images, often failing to distinguish subtle visual differences and fine-grained object relations.
Innovation Solution
The implementation of a modularized graph attentive neural network (MGANN) system that receives textual queries and images, extracts features, mines object relations, and generates outputs indicating target regions that match the query descriptions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If existing image grounding technologies are used, then the system can identify objects in images, but it fails to precisely match complex textual expressions with visual properties and distinguish subtle visual differences
Solution Approach 1:
The patent divides the image grounding task into multiple specialized modules: a text encoding module for processing textual expressions, a visual feature extraction module for capturing image properties, and a matching module for aligning text with visual regions. This segmentation allows each module to specialize in specific aspects of the grounding task, improving precision without requiring a monolithic complex architecture.
Solution Approach 2:
The patent employs attention mechanisms that serve multiple functions: they weigh the importance of different text tokens, identify relevant visual regions, and capture relationships between text and image elements. This multi-functionality allows a single mechanism to address various aspects of the grounding problem, improving precision while managing complexity through reusable components.
2Loss of information
If existing image grounding technologies are used, then the system can process images, but it fails to model fine-grained object relations
Solution Approach 1:
The patent implements attention mechanisms that create feedback loops between text encoding and visual feature extraction. The attention weights generated from text representations are used to refine visual feature extraction, and vice versa, allowing the system to iteratively improve its understanding of object relations and maintain fine-grained information throughout the processing pipeline.
Solution Approach 2:
The patent employs hierarchical feature extraction where low-level visual features are nested within mid-level object features, which are in turn nested within high-level semantic representations. This nested structure allows the system to preserve fine-grained object relation information at lower levels while building up to comprehensive scene understanding at higher levels.
Data Source
AI summary
A system may include a memory and a processor in communication with the memory. The processor may be configured to perform operations. The operations may include receiving an input, extracting features from the input, and mining object relations using the features. The operations may include determining feature vectors using the object relations and generating, using the feature vectors, an output indicating a target region, wherein the target region corresponds to the input.