International business machines corporation (20240111950). MODULARIZED ATTENTIVE GRAPH NETWORKS FOR FINE-GRAINED REFERRING EXPRESSION COMPREHENSION simplified abstract
Contents
- 1 MODULARIZED ATTENTIVE GRAPH NETWORKS FOR FINE-GRAINED REFERRING EXPRESSION COMPREHENSION
- 1.1 Organization Name
- 1.2 Inventor(s)
- 1.3 MODULARIZED ATTENTIVE GRAPH NETWORKS FOR FINE-GRAINED REFERRING EXPRESSION COMPREHENSION - A simplified explanation of the abstract
- 1.4 Simplified Explanation
- 1.5 Potential Applications
- 1.6 Problems Solved
- 1.7 Benefits
- 1.8 Potential Commercial Applications
- 1.9 Possible Prior Art
- 1.10 Original Abstract Submitted
MODULARIZED ATTENTIVE GRAPH NETWORKS FOR FINE-GRAINED REFERRING EXPRESSION COMPREHENSION
Organization Name
international business machines corporation
Inventor(s)
Zhenfang Chen of Cambridge MA (US)
Chuang Gan of Cambridge MA (US)
Dakuo Wang of Cambridge MA (US)
MODULARIZED ATTENTIVE GRAPH NETWORKS FOR FINE-GRAINED REFERRING EXPRESSION COMPREHENSION - A simplified explanation of the abstract
This abstract first appeared for US patent application 20240111950 titled 'MODULARIZED ATTENTIVE GRAPH NETWORKS FOR FINE-GRAINED REFERRING EXPRESSION COMPREHENSION
Simplified Explanation
The computer-implemented method described in the abstract involves fine-grained referring expression comprehension by processing textual expressions and images to extract object relations using language-guided graph neural networks. Here is a simplified explanation of the abstract:
- Decompose textual expression into modules
- Extract visual regional proposals from the image
- Mine object relations using graph neural networks
- Aggregate similarities between textual modules and object relations
Potential Applications
This technology could be applied in:
- Image captioning systems
- Visual search engines
- Content-based image retrieval
Problems Solved
This technology addresses:
- Ambiguity in referring expressions
- Complex object relationships in images
- Improving accuracy in image understanding
Benefits
The benefits of this technology include:
- Enhanced image understanding
- Improved search relevance
- Better user experience in visual applications
Potential Commercial Applications
Commercial applications for this technology may include:
- E-commerce platforms for image search
- Social media platforms for content tagging
- Visual content creation tools
Possible Prior Art
One possible prior art in this field is the use of convolutional neural networks for image recognition and natural language processing for text understanding. However, the specific combination of decomposing textual expressions, extracting visual proposals, and mining object relations using graph neural networks may be novel.
Unanswered Questions
How does this technology handle multi-modal inputs in real-time applications?
The abstract does not specify the processing time required for the fine-grained referring expression comprehension. It would be interesting to know if this method can handle real-time applications efficiently.
What are the limitations of this technology in handling complex and abstract concepts in images and text?
The abstract mentions extracting visual regional proposals and aggregating similarities, but it does not delve into the challenges faced when dealing with abstract or complex concepts that may not have clear visual representations. Understanding the limitations of this technology is crucial for its practical implementation.
Original Abstract Submitted
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.
