View source for 17936097. MODULARIZED ATTENTIVE GRAPH NETWORKS FOR FINE-GRAINED REFERRING EXPRESSION COMPREHENSION simplified abstract (International Business Machines Corporation)

=MODULARIZED ATTENTIVE GRAPH NETWORKS FOR FINE-GRAINED REFERRING EXPRESSION COMPREHENSION=


==Organization Name==

[[:Category:International Business Machines Corporation|International Business Machines Corporation]]


[[Category:International Business Machines Corporation]]

==Inventor(s)==

[[:Category:Zhenfang Chen of Cambridge MA (US)|Zhenfang Chen of Cambridge MA (US)]][[Category:Zhenfang Chen of Cambridge MA (US)]]

[[:Category:Chuang Gan of Cambridge MA (US)|Chuang Gan of Cambridge MA (US)]][[Category:Chuang Gan of Cambridge MA (US)]]

[[:Category:Bo Wu of Cambridge MA (US)|Bo Wu of Cambridge MA (US)]][[Category:Bo Wu of Cambridge MA (US)]]

[[:Category:Dakuo Wang of Cambridge MA (US)|Dakuo Wang of Cambridge MA (US)]][[Category: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 17936097 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 using textual expressions and images. Here is a simplified explanation of the patent application:

* Decomposing textual expressions into different modules
* Extracting visual regional proposals from images
* Mining fine-grained object relations using language-guided graph neural networks
* Aggregating matching similarities between textual modules and object relations

== Potential Applications ==
This technology could be applied in various fields such as computer vision, natural language processing, and artificial intelligence.

== Problems Solved ==
This technology helps in understanding and interpreting referring expressions in a more detailed and accurate manner, improving communication between humans and machines.

== Benefits ==
The method enhances the comprehension of complex referring expressions, leading to better object recognition and understanding in visual and textual data.

== Potential Commercial Applications ==
* Enhanced image and text search engines
* Improved virtual assistants for better understanding user queries

== Possible Prior Art ==
There may be prior art related to object recognition, natural language processing, and image-text understanding technologies that could be relevant to this patent application.

=== Unanswered Questions ===

=== How does this technology compare to existing methods in fine-grained referring expression comprehension? ===
This article does not provide a direct comparison with existing methods in the field.

=== What are the potential limitations or challenges of implementing this technology in real-world applications? ===
The article does not address the potential limitations or challenges that may arise when implementing this technology in practical settings.


==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.



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