Adobe, Inc.

Sudhakar Pandey

Jayant Kumar

Kerem Can Turgutlu

Kannan Iyer

Rohith Mohan Dodle

Gaurav Kukal

Amine Ben Khalifa

Peter J Baust

Archit Kalra

Vera Lychagina

Mina Doroudi

Tarun Vashisth

Sharad Mangalick

Charles Menguy

David Meier Weinstein

Ravi Prakash Singh

Roshan Singh

Ankush Sharma

Disclosed are systems and methods for dynamically determining categories for images. A computer-implemented method may include training a neural network to receive an input image and determine one or more image categories associated with the input image; obtaining a set of images associated with a user; determining, using the trained neural network, one or more image categories associated with each image included in the obtained set of images; determining one or more dominant image categories associated with the user based on the determined image categories for the obtained set of images; and determining an image editing user interface for the user based on the determined one or more dominant image categories.

Systems and methods for dynamic image category determination

Disclosed are systems and methods for dynamically determining categories for images. A computer-implemented method includes training a neural network to receive an input image and determine one or more image categories associated with the input image; obtaining a set of images associated with a user; and determining, using the trained neural network, one or more image categories associated with each image included in the obtained set of images.

Systems and techniques for delivery of contextual interest from interaction information are described that process user interactions with digital content to generate user interest scores for various topics. A contextual user interest system uses user interaction data to identify and contextualize content, and assigns propensity scores to the contextualized content. By dynamically contextualizing pages of content, the contextual user interest system may adapt to changes in the content and provide more accurate and robust information over time, which is not possible using conventional techniques. The contextualized pages of content are used to assign user interest scores across a number of topics to users who have visited the pages of content, and the user interest scores are normalized in a manner that allows a user's degree of interest in a topic to be compared to that of another user.

Delivery of contextual interest from interaction information

Growing community of inventors

Noida, India

Sudhakar Pandey