APPARATUS AND METHOD FOR SHARING AND PRUNING WEIGHTS FOR VISION AND LANGUAGE MODELS

Organization Name

Inventor(s)

APPARATUS AND METHOD FOR SHARING AND PRUNING WEIGHTS FOR VISION AND LANGUAGE MODELS - A simplified explanation of the abstract

This abstract first appeared for US patent application 18368353 titled 'APPARATUS AND METHOD FOR SHARING AND PRUNING WEIGHTS FOR VISION AND LANGUAGE MODELS

Simplified Explanation

The patent application describes a method for performing multimodal tasks using a multimodal model that includes a text encoder and a vision encoder. The method involves obtaining a text feature from a query using the text encoder, obtaining an image feature from one or more input images using the vision encoder, and outputting a response to the query based on the similarity between the text feature and the image feature. The weight vectors of the text encoder and vision encoder are pruned and shared according to a sharing vector and a pruning vector generated by a hypernetwork. The hypernetwork and the multimodal model are jointly trained to minimize differences between weight vectors and the number of parameters in the model.

The method involves using a multimodal model with a text encoder and a vision encoder to process queries that include both text and images.
The text feature is obtained from the query using the text encoder, while the image feature is obtained from input images using the vision encoder.
The response to the query is generated based on the similarity between the text feature and the image feature.
Weight vectors of the text encoder and vision encoder are pruned and shared according to vectors generated by a hypernetwork.
The hypernetwork and multimodal model are trained together to minimize differences in weight vectors and parameters.

Potential Applications

This technology could be applied in various fields such as:

Image and text retrieval systems
Multimodal chatbots
Content-based recommendation systems

Problems Solved

This technology helps in:

Improving the accuracy of multimodal tasks
Enhancing the understanding of queries that include both text and images
Optimizing the performance of multimodal models

Benefits

The benefits of this technology include:

Efficient processing of multimodal queries
Enhanced user experience in interacting with multimodal systems
Improved relevance and accuracy in generating responses to queries

Potential Commercial Applications

This technology has potential commercial applications in:

E-commerce for product recommendations based on images and text
Social media platforms for content recommendations
Customer service chatbots for handling queries with text and images

Possible Prior Art

One possible prior art for this technology could be the use of multimodal models in natural language processing and computer vision tasks. Researchers have explored the integration of text and image features in various applications to improve performance and accuracy.

Unanswered Questions

How does this method compare to existing multimodal models in terms of efficiency and accuracy?

This article does not provide a direct comparison with existing multimodal models in terms of efficiency and accuracy. Further research or experimentation may be needed to evaluate the performance of this method against other approaches.

What are the potential limitations or challenges in implementing this method in real-world applications?

The article does not address potential limitations or challenges in implementing this method in real-world applications. Factors such as computational resources, data requirements, and scalability could be important considerations for practical deployment.

Original Abstract Submitted

A method of performing a multimodal tasks by using a multimodal model that includes a text encoder and a vision encoder, may include obtaining a text feature from the query via the text encoder; obtaining an image feature from the one or more input images via the vision encoder; and outputting a response to the query based on similarity between the text feature and the image feature, wherein weights vectors of the text encoder and the vision encoder are pruned and shared according to a sharing vector and a pruning vector that are generated by a hypernetwork, and wherein the hypernetwork and the multimodal model are jointly trained to minimize at least one of a difference between the weight vectors in the text encoder and the vision encoder, a difference between the weight vectors in different layers of the text encoder, and a number of parameters in the multimodal model.

18368353. APPARATUS AND METHOD FOR SHARING AND PRUNING WEIGHTS FOR VISION AND LANGUAGE MODELS simplified abstract (SAMSUNG ELECTRONICS CO., LTD.)

Contents

APPARATUS AND METHOD FOR SHARING AND PRUNING WEIGHTS FOR VISION AND LANGUAGE MODELS

Organization Name

Inventor(s)