公开(公告)号：US11854119B2

公开(公告)日：2023-12-26

申请号：US17155570

申请日：2021-01-22

Applicant: Adobe Inc.

Inventor： Siavash Khodadadeh ,  Zhe Lin ,  Shabnam Ghadar ,  Saeid Motiian ,  Richard Zhang ,  Ratheesh Kalarot ,  Baldo Faieta

IPC: G06N3/08 ,  G06T7/90 ,  G06N3/045 ,  G06T11/00

CPC classification number: G06T11/001 ,  G06N3/045 ,  G06N3/08 ,  G06T7/90

Abstract: Embodiments are disclosed for automatic object re-colorization in images. In some embodiments, a method of automatic object re-colorization includes receiving a request to recolor an object in an image, the request including an object identifier and a color identifier, identifying an object in the image associated with the object identifier, generating a mask corresponding to the object in the image, providing the image, the mask, and the color identifier to a color transformer network, the color transformer network trained to recolor objects in input images, and generating, by the color transformer network, a recolored image, wherein the object in the recolored image has been recolored to a color corresponding to the color identifier.

公开(公告)号：US20230386114A1

公开(公告)日：2023-11-30

申请号：US18449604

申请日：2023-08-14

Applicant: Adobe Inc.

Inventor： Akhilesh Kumar ,  Baldo Faieta ,  Piotr Walczyszyn ,  Ratheesh Kalarot ,  Archie Bagnall ,  Shabnam Ghadar ,  Wei-An Lin ,  Cameron Smith ,  Christian Cantrell ,  Patrick Hebron ,  Wilson Chan ,  Jingwan Lu ,  Holger Winnemoeller ,  Sven Olsen

IPC: G06T11/60 ,  G06T11/20 ,  G06N3/04

CPC classification number: G06T11/60 ,  G06N3/04 ,  G06T11/203

Abstract: The present disclosure describes systems, methods, and non-transitory computer readable media for detecting user interactions to edit a digital image from a client device and modify the digital image for the client device by using a web-based intermediary that modifies a latent vector of the digital image and an image modification neural network to generate a modified digital image from the modified latent vector. In response to user interaction to modify a digital image, for instance, the disclosed systems modify a latent vector extracted from the digital image to reflect the requested modification. The disclosed systems further use a latent vector stream renderer (as an intermediary device) to generate an image delta that indicates a difference between the digital image and the modified digital image. The disclosed systems then provide the image delta as part of a digital stream to a client device to quickly render the modified digital image.

公开(公告)号：US11823490B2

公开(公告)日：2023-11-21

申请号：US17341778

申请日：2021-06-08

Applicant: ADOBE INC.

Inventor： Ratheesh Kalarot ,  Siavash Khodadadeh ,  Baldo Faieta ,  Shabnam Ghadar ,  Saeid Motiian ,  Wei-An Lin ,  Zhe Lin

IPC: G06V40/16 ,  G06N3/084 ,  G06T11/60 ,  G06N3/045

CPC classification number: G06V40/169 ,  G06N3/045 ,  G06N3/084 ,  G06T11/60

Abstract: Systems and methods for image processing are described. One or more embodiments of the present disclosure identify a latent vector representing an image of a face, identify a target attribute vector representing a target attribute for the image, generate a modified latent vector using a mapping network that converts the latent vector and the target attribute vector into a hidden representation having fewer dimensions than the latent vector, wherein the modified latent vector is generated based on the hidden representation, and generate a modified image based on the modified latent vector, wherein the modified image represents the face with the target attribute.

公开(公告)号：US20230316606A1

公开(公告)日：2023-10-05

申请号：US17655739

申请日：2022-03-21

Applicant: Adobe Inc.

Inventor： Hui Qu ,  Baldo Faieta ,  Cameron Smith ,  Elya Shechtman ,  Jingwan Lu ,  Ratheesh Kalarot ,  Richard Zhang ,  Saeid Motiian ,  Shabnam Ghadar ,  Wei-An Lin

IPC: G06N3/04 ,  G06T11/60

CPC classification number: G06T11/60 ,  G06N3/0454

Abstract: The present disclosure relates to systems, non-transitory computer-readable media, and methods for latent-based editing of digital images using a generative neural network. In particular, in one or more embodiments, the disclosed systems perform latent-based editing of a digital image by mapping a feature tensor and a set of style vectors for the digital image into a joint feature style space. In one or more implementations, the disclosed systems apply a joint feature style perturbation and/or modification vectors within the joint feature style space to determine modified style vectors and a modified feature tensor. Moreover, in one or more embodiments the disclosed systems generate a modified digital image utilizing a generative neural network from the modified style vectors and the modified feature tensor.

公开(公告)号：US11741157B2

公开(公告)日：2023-08-29

申请号：US17544689

申请日：2021-12-07

Applicant: Adobe Inc.

Inventor： Ajinkya Kale ,  Baldo Faieta ,  Benjamin Leviant ,  Fengbin Chen ,  Francois Guerin ,  Kate Sousa ,  Trung Bui ,  Venkat Barakam ,  Zhe Lin

IPC: G06F16/40 ,  G06F16/58 ,  G06F16/48 ,  G06F16/2457 ,  G06F16/43 ,  G06V20/00 ,  G06F18/23213

CPC classification number: G06F16/5866 ,  G06F16/24578 ,  G06F16/43 ,  G06F16/48 ,  G06F18/23213 ,  G06V20/35 ,  G06V2201/10

Abstract: Systems, methods, and non-transitory computer-readable media are disclosed for determining multi-term contextual tags for digital content and propagating the multi-term contextual tags to additional digital content. For instance, the disclosed systems can utilize search query supervision to determine and associate multi-term contextual tags (e.g., tags that represent a specific concept based on the order of the terms in the tag) with digital content. Furthermore, the disclosed systems can propagate the multi-term contextual tags determined for the digital content to additional digital content based on similarities between the digital content and additional digital content (e.g., utilizing clustering techniques). Additionally, the disclosed systems can provide digital content as search results based on the associated multi-term contextual tags.

公开(公告)号：US11709885B2

公开(公告)日：2023-07-25

申请号：US17025041

申请日：2020-09-18

Applicant: Adobe Inc.

Inventor： John Collomosse ,  Zhe Lin ,  Saeid Motiian ,  Hailin Jin ,  Baldo Faieta ,  Alex Filipkowski

IPC: G06T7/00 ,  G06F16/583 ,  G06F16/532 ,  G06N3/08 ,  G06F16/535 ,  G06V10/82 ,  G06V20/30

CPC classification number: G06F16/5854 ,  G06F16/532 ,  G06F16/535 ,  G06F16/5838 ,  G06N3/08 ,  G06V10/82 ,  G06V20/30

Abstract: The present disclosure relates to systems, methods, and non-transitory computer readable media for accurately and flexibly identifying digital images with similar style to a query digital image using fine-grain style determination via weakly supervised style extraction neural networks. For example, the disclosed systems can extract a style embedding from a query digital image using a style extraction neural network such as a novel two-branch autoencoder architecture or a weakly supervised discriminative neural network. The disclosed systems can generate a combined style embedding by combining complementary style embeddings from different style extraction neural networks. Moreover, the disclosed systems can search a repository of digital images to identify digital images with similar style to the query digital image. The disclosed systems can also learn parameters for one or more style extraction neural network through weakly supervised training without a specifically labeled style ontology for sample digital images.

公开(公告)号：US20230206525A1

公开(公告)日：2023-06-29

申请号：US18117155

申请日：2023-03-03

Applicant: Adobe Inc.

Inventor： Midhun Harikumar ,  Pranav Aggarwal ,  Baldo Faieta ,  Ajinkya Kale ,  Zhe Lin

IPC: G06T11/60 ,  G06T7/11 ,  G06T7/162

CPC classification number: G06T11/60 ,  G06T7/11 ,  G06T7/162 ,  G06T2207/20084 ,  G06T2207/20081

Abstract: A non-transitory computer-readable medium includes program code that is stored thereon. The program code is executable by one or more processing devices for performing operations including generating, using a model, a learned image representation of a target image. The operations further include generating, using a text embedding model, a text embedding of a text query. The text embedding and the learned image representation of the target image are in a same embedding space. Additionally, the operations include convolving the learned image representation of the target image with the text embedding of the text query. Moreover, the operations include generating an object-segmented image based on the convolving of the learned image representation of the target image with the text embedding.

公开(公告)号：US20230076196A1

公开(公告)日：2023-03-09

申请号：US17466699

申请日：2021-09-03

Applicant: ADOBE INC.

Inventor： Akhilesh Kumar ,  Ratheesh Kalarot ,  Baldo Faieta ,  Shabnam Ghadar

IPC: G06T11/60 ,  G06N3/04 ,  G06T3/40 ,  H04L29/06

Abstract: Embodiments of the present invention provide systems, methods, and computer storage media for editing images using a web-based intermediary between a user interface on a client device and an image editing neural network(s) (e.g., a generative adversarial network) on a server(s). The present image editing system supports multiple users in the same software container, advanced concurrency of projection and transformation of the same image, clubbing transformation requests from several users hosted in the same software container, and smooth display updates during a progressive projection.

公开(公告)号：US20220253990A1

公开(公告)日：2022-08-11

申请号：US17172744

申请日：2021-02-10

Applicant: ADOBE INC.

Inventor： Akhilesh Kumar ,  Zhe Lin ,  Baldo Faieta

IPC: G06T5/20 ,  G06N3/04 ,  G06K9/62 ,  G06T7/00

Abstract: The present disclosure describes systems and methods for image enhancement. Embodiments of the present disclosure provide an image enhancement system with a feedback mechanism that provides quantifiable image enhancement information. An image enhancement system may include a discriminator network that determines the quality of the media object. In cases where the discriminator network determines that the media object has a low image quality score (e.g., an image quality score below a quality threshold), the image enhancement system may perform enhancement on the media object using an enhancement network (e.g., using an enhancement network that includes a generative neural network or a generative adversarial network (GAN) model). The discriminator network may then generate an enhancement score for the enhanced media object that may be provided to the user as a feedback mechanism (e.g., where the enhancement score generated by the discriminator network quantifies the enhancement performed by the enhancement network).

公开(公告)号：US20220122306A1

公开(公告)日：2022-04-21

申请号：US17468487

申请日：2021-09-07

Applicant: Adobe Inc.

Inventor： Wei-An Lin ,  Baldo Faieta ,  Cameron Smith ,  Elya Shechtman ,  Jingwan Lu ,  Jun-Yan Zhu ,  Niloy Mitra ,  Ratheesh Kalarot ,  Richard Zhang ,  Shabnam Ghadar ,  Zhixin Shu

IPC: G06T11/60 ,  G06F3/0484 ,  G06N3/08 ,  G06N3/04

Abstract: Systems and methods dynamically adjust an available range for editing an attribute in an image. An image editing system computes a metric for an attribute in an input image as a function of a latent space representation of the input image and a filtering vector for editing the input image. The image editing system compares the metric to a threshold. If the metric exceeds the threshold, then the image editing system selects a first range for editing the attribute in the input image. If the metric does not exceed the threshold, a second range is selected. The image editing system causes display of a user interface for editing the input image comprising an interface element for editing the attribute within the selected range.