Adobe, Inc.

Kannan Iyer

Jayant Kumar

Rohith Mohan Dodle

Gaurav Kukal

Mina Doroudi

Archit Kalra

Vera Lychagina

Kerem Can Turgutlu

Sudhakar Pandey

Amine Ben Khalifa

Peter J Baust

Sharad Mangalick

Tarun Vashisth

Zhe Lin

Balaji Vasan Srinivasan

Shabnam Ghadar

Saeid Motiian

Midhun Harikumar

Daniel Miranda

Anandhavelu N

Shankar Srinivasan

Vipul Dalal

Chad Michael Krsek

Prethebha Chandrasegaran

Charles R McKinney

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

The present disclosure relates to an image merging system that automatically and seamlessly detects and merges missing people for a set of digital images into a composite group photo. For instance, the image merging system utilizes a number of models and operations to automatically analyze multiple digital images to identify a missing person from a base image, segment the missing person from the second image, and generate a composite group photo by merging the segmented image of the missing person into the base image. In this manner, the image merging system automatically creates merged group photos that appear natural and realistic.

Automatically merging people and objects from multiple digital images to generate a composite digital image

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.

The present disclosure is directed toward a message classification system that allows for improved customer service through intelligent classification of social media messages. For example, the message classification system may detect one or more messages that share a common context from within a group of messages, organize the detected messages into a messaging thread, and analyze messages within the messaging thread to identify a messaging thread classification. Further, the message classification system may analyze users participating in messaging threads to determine user classifications. Using the classified messaging thread and/or user classifications, the message classification system may assist an entity in improving social media customer service.

Organizing and classifying social media conversations to improve customer service

Techniques are disclosed for automatically modeling and predicting moderator actions for online content. A model can be generated or updated based on the content received and the action or actions taken by the moderator in response to receiving the content. The model can be used to automatically predict which action, or combination of actions, are likely to be taken by the moderator when new content is received, and suggest those action(s) to the moderator. These suggestions can, among other things, simplify and speed up the decision-making process for the moderator.

Automatic moderation of online content

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San Ramon, CA, United States of America

Kannan Iyer