Apple inc. (20240282107). AUTOMATIC IMAGE SELECTION WITH CROSS MODAL MATCHING simplified abstract

AUTOMATIC IMAGE SELECTION WITH CROSS MODAL MATCHING

Organization Name

apple inc.

Inventor(s)

Jia Huang of Mountain View CA (US)

Robert J. Monarch of San Francisco CA (US)

Alex Jungho Kim of Mukilteo WA (US)

Jungsuk Kwac of Palo Alto CA (US)

Parmeshwar Khurd of San Jose CA (US)

Kailash Thiyagarajan of Dallas TX (US)

Xiaoyuan Goodman Gu of San Jose CA (US)

AUTOMATIC IMAGE SELECTION WITH CROSS MODAL MATCHING - A simplified explanation of the abstract

This abstract first appeared for US patent application 20240282107 titled 'AUTOMATIC IMAGE SELECTION WITH CROSS MODAL MATCHING

The present technology involves a multi-modal transformer model trained for cross-modal tasks like image-text matching, refined with data specific to the downstream use case.

• The model is designed to perform tasks such as image-text matching.
• It is refined with labeled examples from a dataset of text-image pairs.
• The technology can be applied to advertising applications in an app store.
• The model is refined with examples of images used in app store advertisements.
• The goal is to achieve desired interactions in the proper context.

Potential Applications: This technology can be used in various fields such as advertising, e-commerce, content recommendation systems, and multimedia search engines.

Problems Solved: The technology addresses the challenge of accurately matching images with text in various applications, improving user engagement and conversion rates.

Benefits: The technology can enhance the effectiveness of advertising campaigns, improve user experience, and increase the relevance of search results in multimedia applications.

Commercial Applications: "Multi-Modal Transformer Model for Image-Text Matching in Advertising Applications"

Frequently Updated Research: Researchers are constantly exploring new ways to improve the performance and efficiency of multi-modal transformer models for various cross-modal tasks.

Questions about Multi-Modal Transformer Model for Image-Text Matching in Advertising Applications:

1. How does the technology improve user engagement in advertising applications? The technology enhances user engagement by accurately matching images with text, making advertisements more relevant and appealing to users.

2. What are the potential implications of this technology for e-commerce platforms? This technology can significantly improve the effectiveness of product recommendations, leading to increased sales and customer satisfaction in e-commerce platforms.

Original Abstract Submitted

the present technology pertains to a multi-modal transformer model that is designed and trained to perform cross-modal tasks such as image-text matching, wherein the model is further refined with data for the particular downstream use case of the model. more specifically, the present technology can refine the underlying model with labeled examples derived from a dataset of text-image pairs that ultimately achieved a desired interaction in the proper context. for example, in the use case of advertising applications in an app store, the present technology can refine the underlying model with examples of images used to advertise applications in the app store where the respective invitational content was clicked or converted.