Adobe, Inc.

Yinglan Ma

Zhe Lin

Jianming Zhang

Kalyan Krishna Sunkavalli

Jose Ignacio Echevarria Vallespi

He Zhang

Chih-Yao Hsieh

Elya Shechtman

Franck Dernoncourt

Trung Huu Bui

Sylvain Philippe Paris

Yilin Wang

Sarah Kong

Aliakbar Darabi

Doo Soon Kim

Zijun Wei

Seokhwan Kim

Hyunghwan Byun

Frederick Mandia

Meredith Payne Stotzner

Oliver Markus Michael Brdiczka

Ripul Bhutani

Jeya Maria Jose Valanarasu

Zahra Rahimi

The present disclosure relates to systems, non-transitory computer-readable media, and methods that implement a dual-branched neural network architecture to harmonize composite images. For example, in one or more implementations, the transformer-based harmonization system uses a convolutional branch and a transformer branch to generate a harmonized composite image based on an input composite image and a corresponding segmentation mask. More particularly, the convolutional branch comprises a series of convolutional neural network layers followed by a style normalization layer to extract localized information from the input composite image. Further, the transformer branch comprises a series of transformer neural network layers to extract global information based on different resolutions of the input composite image. Utilizing a decoder, the transformer-based harmonization system combines the local information and the global information from the corresponding convolutional branch and transformer branch to generate a harmonized composite image.

Harmonizing composite images utilizing a transformer neural network

The present disclosure relates to systems, methods, and non-transitory computer readable media for accurately and flexibly generating harmonized digital images utilizing an object-to-object harmonization neural network. For example, the disclosed systems implement, and learn parameters for, an object-to-object harmonization neural network to combine a style code from a reference object with features extracted from a target object. Indeed, the disclosed systems extract a style code from a reference object utilizing a style encoder neural network. In addition, the disclosed systems generate a harmonized target object by applying the style code of the reference object to a target object utilizing an object-to-object harmonization neural network.

Object-to-object harmonization for digital images

In implementations of systems for generating suggestions with knowledge graph embedding vectors, a computing device implements a suggestion system to receive input data describing user interactions with an application for editing digital content. The suggestion system generates input embedding vectors based on the user interactions with the application and determines an item based on the input embedding vectors and knowledge graph embedding vectors generated from nodes of a knowledge graph describing a tutorial for editing digital content. The suggestion system generates an indication of the item for display in a user interface of a display device.

Systems for generating suggestions with knowledge graph embedding vectors

This application relates generally to modifying visual data based on audio commands and more specifically, to performing complex operations that modify visual data based on one or more audio commands. In some embodiments, a computer system may receive an audio input and identify an audio command based on the audio input. The audio command may be mapped to one or more operations capable of being performed by a multimedia editing application. The computer system may perform the one or more operations to edit to received multimedia data.

Voice interaction for image editing

The present disclosure relates to systems, non-transitory computer-readable media, and methods that generate ground truth annotations of target utterances in digital image editing dialogues in order to create a state-driven training data set. In particular, in one or more embodiments, the disclosed systems utilize machine and user defined tags, machine learning model predictions, and user input to generate a ground truth annotation that includes frame information in addition to intent, attribute, object, and/or location information. In at least one embodiment, the disclosed systems generate ground truth annotations in conformance with an annotation ontology that results in fast and accurate digital image editing dialogue annotation.

Generating ground truth annotations corresponding to digital image editing dialogues for training state tracking models

Methods and systems are provided for adjusting the brightness of images. In some implementations, an exposure bracketed set of input images produced by a camera is received. A brightness adjustment is determined for at least one input image from the set of input images. The determined brightness adjustment is applied to the input image. An output image is produced by exposure fusion from the set of input images, using the input image having the determined brightness adjustment. The output image is transmitted where, the transmitting causes display of the output image on a user device.

Preserving color in image brightness adjustment for exposure fusion

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Mountain View, CA, United States of America

Yinglan Ma