公开(公告)号：US20220391633A1

公开(公告)日：2022-12-08

申请号：US17337194

申请日：2021-06-02

Applicant: Adobe Inc.

Inventor： Midhun Harikumar ,  Zhe Lin ,  Shabnam Ghadar ,  Baldo Faieta

IPC: G06K9/62 ,  G06N3/08

Abstract: Methods, systems, and non-transitory computer readable media are disclosed for accurately and efficiently generating groups of images portraying semantically similar objects for utilization in building machine learning models. In particular, the disclosed system utilizes metadata and spatial statistics to extract semantically similar objects from a repository of digital images. In some embodiments, the disclosed system generates color embeddings and content embeddings for the identified objects. The disclosed system can further group similar objects together within a query space by utilizing a clustering algorithm to create object clusters and then refining and combining the object clusters within the query space. In some embodiments, the disclosed system utilizes one or more of the object clusters to build a machine learning model.

公开(公告)号：US20220318420A1

公开(公告)日：2022-10-06

申请号：US17217024

申请日：2021-03-30

Applicant: ADOBE INC.

Inventor： William Marino ,  Tim Converse ,  Sudharshan reddy Kakumanu ,  Shabnam Ghadar ,  Nico Becherer ,  Dhaval Shah ,  Ben Bowles ,  Alvin Ghouas ,  Alexander Riss

IPC: G06F21/62 ,  G06F21/31 ,  G06N20/00

Abstract: The present disclosure describes systems and methods for a privacy sensitive computing system. One or more embodiments provide a protected computing environment, a code authorization unit, and a data aggregation unit. For example, some embodiments of the privacy sensitive computing system may train unsupervised or self-supervised ML models on user-generated assets subject to privacy considerations that mandate those assets are not viewed directly by human eyes.

公开(公告)号：US11436865B1

公开(公告)日：2022-09-06

申请号：US17207178

申请日：2021-03-19

Applicant: Adobe Inc.

Inventor： Saeid Motiian ,  Zhe Lin ,  Shabnam Ghadar ,  Baldo Faieta

IPC: G06V40/16 ,  G06K9/62 ,  G06V30/194 ,  G06V40/10

Abstract: Embodiments are disclosed for finding similar persons in images. In particular, in one or more embodiments, the disclosed systems and methods comprise receiving an image query, the image query including an input image that includes a representation of a person, generating a first cropped image including a representation of the person's face and a second cropped image including a representation of the person's body, generating an image embedding for the input image by combining a face embedding corresponding to the first cropped image and a body embedding corresponding to the second cropped image, and querying an image repository in embedding space by comparing the image embedding to a plurality of image embeddings associated with a plurality of images in the image repository to obtain one or more images based on similarity to the input image in the embedding space.

公开(公告)号：US20220122305A1

公开(公告)日：2022-04-21

申请号：US17384273

申请日：2021-07-23

Applicant: Adobe Inc.

Inventor： Cameron Smith ,  Ratheesh Kalarot ,  Wei-An Lin ,  Richard Zhang ,  Niloy Mitra ,  Elya Shechtman ,  Shabnam Ghadar ,  Zhixin Shu ,  Yannick Hold-Geoffrey ,  Nathan Carr ,  Jingwan Lu ,  Oliver Wang ,  Jun-Yan Zhu

IPC: G06T11/60 ,  G06T3/40

Abstract: An improved system architecture uses a pipeline including an encoder and a Generative Adversarial Network (GAN) including a generator neural network to generate edited images with improved speed, realism, and identity preservation. The encoder produces an initial latent space representation of an input image by encoding the input image. The generator neural network generates an initial output image by processing the initial latent space representation of the input image. The system generates an optimized latent space representation of the input image using a loss minimization technique that minimizes a loss between the input image and the initial output image. The loss is based on target perceptual features extracted from the input image and initial perceptual features extracted from the initial output image. The system outputs the optimized latent space representation of the input image for downstream use.

公开(公告)号：US20220122222A1

公开(公告)日：2022-04-21

申请号：US17384283

申请日：2021-07-23

Applicant: Adobe Inc.

Inventor： Cameron Smith ,  Ratheesh Kalarot ,  Wei-An Lin ,  Richard Zhang ,  Niloy Mitra ,  Elya Shechtman ,  Shabnam Ghadar ,  Zhixin Shu ,  Yannick Hold-Geoffrey ,  Nathan Carr ,  Jingwan Lu ,  Oliver Wang ,  Jun-Yan Zhu

IPC: G06T3/40 ,  G06T11/60

Abstract: An improved system architecture uses a Generative Adversarial Network (GAN) including a specialized generator neural network to generate multiple resolution output images. The system produces a latent space representation of an input image. The system generates a first output image at a first resolution by providing the latent space representation of the input image as input to a generator neural network comprising an input layer, an output layer, and a plurality of intermediate layers and taking the first output image from an intermediate layer, of the plurality of intermediate layers of the generator neural network. The system generates a second output image at a second resolution different from the first resolution by providing the latent space representation of the input image as input to the generator neural network and taking the second output image from the output layer of the generator neural network.

公开(公告)号：US20220121932A1

公开(公告)日：2022-04-21

申请号：US17384378

申请日：2021-07-23

Applicant: Adobe Inc.

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

IPC: G06N3/08 ,  G06N3/04

Abstract: Systems and methods train an encoder neural network for fast and accurate projection into the latent space of a Generative Adversarial Network (GAN). The encoder is trained by providing an input training image to the encoder and producing, by the encoder, a latent space representation of the input training image. The latent space representation is provided as input to the GAN to generate a generated training image. A latent code is sampled from a latent space associated with the GAN and the sampled latent code is provided as input to the GAN. The GAN generates a synthetic training image based on the sampled latent code. The sampled latent code is provided as input to the encoder to produce a synthetic training code. The encoder is updated by minimizing a loss between the generated training image and the input training image, and the synthetic training code and the sampled latent code.

公开(公告)号：US20220121876A1

公开(公告)日：2022-04-21

申请号：US17468498

申请日：2021-09-07

Applicant: Adobe Inc.

Inventor： Ratheesh Kalarot ,  Wei-An Lin ,  Baldo Faieta ,  Shabnam Ghadar

IPC: G06K9/62 ,  G06N3/08 ,  G06N3/04 ,  G06N20/20

Abstract: Systems and methods use a non-linear latent filter neural network for editing an image. An image editing system trains a first neural network by minimizing a loss based upon a predicted attribute value for a target attribute in a training image. The image editing system obtains a latent space representation of an input image to be edited and a target attribute value for the target attribute in the input image. The image editing system provides the latent space representation and the target attribute value as input to the trained first neural network for modifying the target attribute in the input image to generate a modified latent space representation of the input image. The image editing system provides the modified latent space representation as input to a second neural network to generate an output image with a modification to the target attribute corresponding to the target attribute value.

公开(公告)号：US12254594B2

公开(公告)日：2025-03-18

申请号：US17657691

申请日：2022-04-01

Applicant: Adobe Inc.

Inventor： Hui Qu ,  Jingwan Lu ,  Saeid Motiian ,  Shabnam Ghadar ,  Wei-An Lin ,  Elya Shechtman

IPC: G06T5/50 ,  G06N3/04 ,  G06N3/045 ,  G06T7/11

Abstract: Methods, systems, and non-transitory computer readable media are disclosed for intelligently enhancing details in edited images. The disclosed system iteratively updates residual detail latent code for segments in edited images where detail has been lost through the editing process. More particularly, the disclosed system enhances an edited segment in an edited image based on details in a detailed segment of an image. Additionally, the disclosed system may utilize a detail neural network encoder to project the detailed segment and a corresponding segment of the edited image into a residual detail latent code. In some embodiments, the disclosed system generates a refined edited image based on the residual detail latent code and a latent vector of the edited image.

公开(公告)号：US20250069299A1

公开(公告)日：2025-02-27

申请号：US18452827

申请日：2023-08-21

Applicant: ADOBE INC.

Inventor： Kevin Duarte ,  Wei-An Lin ,  Ratheesh Kalarot ,  Shabnam Ghadar ,  Jingwan Lu ,  Elya Shechtman

IPC: G06T11/60

Abstract: One or more aspects of a method, apparatus, and non-transitory computer readable medium include obtaining an input latent vector for an image generation network and a target lighting representation. A modified latent vector is generated based on the input latent vector and the target lighting representation, and an image generation network generates an image based on the modified latent vector using.

公开(公告)号：US12093308B2

公开(公告)日：2024-09-17

申请号：US17453595

申请日：2021-11-04

Applicant: ADOBE INC.

Inventor： Baldo Faieta ,  Ajinkya Gorakhnath Kale ,  Pranav Vineet Aggarwal ,  Naveen Marri ,  Saeid Motiian ,  Tracy Holloway King ,  Alex Filipkowski ,  Shabnam Ghadar

IPC: G06F16/583 ,  G06F16/535 ,  G06F16/538 ,  G06F16/58 ,  G06F40/295 ,  G06N3/08

CPC classification number: G06F16/5838 ,  G06F16/535 ,  G06F16/538 ,  G06F16/5866 ,  G06F40/295 ,  G06N3/08

Abstract: Systems and methods for image retrieval are described. Embodiments of the present disclosure receive a search query from a user; extract an entity and a color phrase describing the entity from the search query; generate an entity color embedding in a color embedding space from the color phrase using a multi-modal color encoder; identify an image in a database based on metadata for the image including an object label corresponding to the extracted entity and an object color embedding in the color embedding space corresponding to the object label; and provide image information for the image to the user based on the metadata.