Adobe, Inc.

Gaurav Kukal

Jayant Kumar

Kannan Iyer

Kerem Can Turgutlu

Rohith Mohan Dodle

Mina Doroudi

Archit Kalra

Amine Ben Khalifa

Vera Lychagina

Vipul Dalal

Sudhakar Pandey

Peter J Baust

Sharad Mangalick

Tarun Vashisth

Zhe Lin

Shabnam Ghadar

Saeid Motiian

Midhun Harikumar

Daniel Miranda

Benjamin Leviant

Fengbin Chen

Venkat Barakam

Prethebha Chandrasegaran

Charles R McKinney

Sumeet Zaverilal Gala

Systems and methods for information retrieval are described. Embodiments generate a dense embedding for each of a plurality of media objects to be searched, generate a sparse embedding for each of the media objects using an encoder that takes the dense embedding as an input, wherein the sparse embedding satisfies a sparsity constraint that is applied to at least one layer of the encoder during training, and perform a search on the plurality of media objects based at least in part on the sparse embedding.

Retrieval aware embedding

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.

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Fremont, CA, United States of America

Gaurav Kukal