公开(公告)号：US11887216B2

公开(公告)日：2024-01-30

申请号：US17455796

申请日：2021-11-19

Applicant: ADOBE INC.

Inventor： Ratheesh Kalarot ,  Timothy M. Converse ,  Shabnam Ghadar ,  John Thomas Nack ,  Jingwan Lu ,  Elya Shechtman ,  Baldo Faieta ,  Akhilesh Kumar

IPC: G06T5/00 ,  G06T11/00 ,  G06N3/08 ,  G06V40/16

CPC classification number: G06T11/00 ,  G06N3/08 ,  G06V40/168 ,  G06V40/172

Abstract: The present disclosure describes systems and methods for image processing. Embodiments of the present disclosure include an image processing apparatus configured to generate modified images (e.g., synthetic faces) by conditionally changing attributes or landmarks of an input image. A machine learning model of the image processing apparatus encodes the input image to obtain a joint conditional vector that represents attributes and landmarks of the input image in a vector space. The joint conditional vector is then modified, according to the techniques described herein, to form a latent vector used to generate a modified image. In some cases, the machine learning model is trained using a generative adversarial network (GAN) with a normalization technique, followed by joint training of a landmark embedding and attribute embedding (e.g., to reduce inference time).

公开(公告)号：US12254597B2

公开(公告)日：2025-03-18

申请号：US17709221

申请日：2022-03-30

Applicant: Adobe Inc.

Inventor： Cameron Smith ,  Wei-An Lin ,  Timothy M. Converse ,  Shabnam Ghadar ,  Ratheesh Kalarot ,  John Nack ,  Jingwan Lu ,  Hui Qu ,  Elya Shechtman ,  Baldo Faieta

IPC: G06T5/50 ,  G06N3/045

Abstract: An item recommendation system receives a set of recommendable items and a request to select, from the set of recommendable items, a contrast group. The item recommendation system selects a contrast group from the set of recommendable items by applying a image modification model to the set of recommendable items. The image modification model includes an item selection model configured to determine an unbiased conversion rate for each item of the set of recommendable items and select a recommended item from the set of recommendable items having a greatest unbiased conversion rate. The image modification model includes a contrast group selection model configured to select, for the recommended item, a contrast group comprising the recommended item and one or more contrast items. The item recommendation system transmits the contrast group responsive to the request.

公开(公告)号：US20230316475A1

公开(公告)日：2023-10-05

申请号：US17709221

申请日：2022-03-30

Applicant: Adobe Inc.

Inventor： Cameron Smith ,  Wei-An Lin ,  Timothy M. Converse ,  Shabnam Ghadar ,  Ratheesh Kalarot ,  John Nack ,  Jingwan Lu ,  Hui Qu ,  Elya Shechtman ,  Baldo Faieta

IPC: G06T5/50 ,  G06N3/04

CPC classification number: G06T5/50 ,  G06N3/0454 ,  G06T2207/20221 ,  G06T2207/20084 ,  G06T2207/20081

Abstract: An item recommendation system receives a set of recommendable items and a request to select, from the set of recommendable items, a contrast group. The item recommendation system selects a contrast group from the set of recommendable items by applying a image modification model to the set of recommendable items. The image modification model includes an item selection model configured to determine an unbiased conversion rate for each item of the set of recommendable items and select a recommended item from the set of recommendable items having a greatest unbiased conversion rate. The image modification model includes a contrast group selection model configured to select, for the recommended item, a contrast group comprising the recommended item and one or more contrast items. The item recommendation system transmits the contrast group responsive to the request.

公开(公告)号：US20230162407A1

公开(公告)日：2023-05-25

申请号：US17455796

申请日：2021-11-19

Applicant: ADOBE INC.

Inventor： Ratheesh Kalarot ,  Timothy M. Converse ,  Shabnam Ghadar ,  John Thomas Nack ,  Jingwan Lu ,  Elya Shechtman ,  Baldo Faieta ,  Akhilesh Kumar

IPC: G06T11/00 ,  G06K9/00 ,  G06N3/08

CPC classification number: G06T11/00 ,  G06K9/00288 ,  G06K9/00268 ,  G06N3/08

Abstract: The present disclosure describes systems and methods for image processing. Embodiments of the present disclosure include an image processing apparatus configured to generate modified images (e.g., synthetic faces) by conditionally changing attributes or landmarks of an input image. A machine learning model of the image processing apparatus encodes the input image to obtain a joint conditional vector that represents attributes and landmarks of the input image in a vector space. The joint conditional vector is then modified, according to the techniques described herein, to form a latent vector used to generate a modified image. In some cases, the machine learning model is trained using a generative adversarial network (GAN) with a normalization technique, followed by joint training of a landmark embedding and attribute embedding (e.g., to reduce inference time).