Microsoft Technology Licensing, LLC

Adobe, Inc.

Julia Gong

Houdong Hu

Jingwan Lu

Yannick Hold-Geoffroy

William Douglas Guyman

Jyotkumar Jagdishbhai Patel

Xuetao Yin

Xiujia Guo

Rajiv S Binwade

Yale Song

A data processing system implements a receiving a textual input comprising a query for a first image. The data processing system also implements analyzing the textual input to determine a predicted color palette associated with a subject matter of the query; and procedurally generating the first image using the predicted color palette. Another implementation of the data processing system implements providing the textual input to a first machine learning model to obtain the first image, the first machine learning model being trained using a dataset comprising abstract imagery and analyzing the textual input using the first machine learning model to obtain the first image in response to receiving the textual input.

Techniques for abstract image generation from multimodal inputs with content appropriateness considerations

The present disclosure provides method and apparatus for determining a food item from a photograph and a corresponding restaurant serving the food item. An image is received from a user, the image being associated with a consumable item. One or more ingredients of the consumable item in the image is identified along with a location of the user and using a neural network, determining one or more similar images from a database. A restaurant associated with each of the one or more similar images is determined along with a similarity score indicating a similarity between the restaurant and the identified content of the image. The one or more restaurants and/or associated similar food items are ranked based on the similarity score and a list of ranked restaurants is provided to the user.

Providing local recommendations based on images of consumable items

Techniques and systems are provided for configuring neural networks to perform warping of an object represented in an image to create a caricature of the object. For instance, in response to obtaining an image of an object, a warped image generator generates a warping field using the image as input. The warping field is generated using a model trained with pairings of training images and known warped images using supervised learning techniques and one or more losses. The warped image generator determines, based on the warping field, a set of displacements associated with pixels of the input image. These displacements indicate pixel displacement directions for the pixels of the input image. These displacements are applied to the digital image to generate a warped image of the object.

Automatic image warping for warped image generation

Growing community of inventors

Cary, NC, United States of America

Julia Gong