公开(公告)号：US20230351566A1

公开(公告)日：2023-11-02

申请号：US17660968

申请日：2022-04-27

Applicant: ADOBE INC.

Inventor： Sangryul Jeon ,  Zhifei Zhang ,  Zhe Lin ,  Scott Cohen ,  Zhihong Ding

IPC: G06T5/50 ,  G06V10/40 ,  G06V10/75 ,  G06V10/77 ,  G06V10/774 ,  G06V10/44

CPC classification number: G06T5/50 ,  G06V10/513 ,  G06V10/751 ,  G06V10/7715 ,  G06V10/774 ,  G06V10/454 ,  G06T2207/20221 ,  G06T2207/20081

Abstract: Systems and methods for image processing are configured. Embodiments of the present disclosure encode a content image and a style image using a machine learning model to obtain content features and style features, wherein the content image includes a first object having a first appearance attribute and the style image includes a second object having a second appearance attribute; align the content features and the style features to obtain a sparse correspondence map that indicates a correspondence between a sparse set of pixels of the content image and corresponding pixels of the style image; and generate a hybrid image based on the sparse correspondence map, wherein the hybrid image depicts the first object having the second appearance attribute.

公开(公告)号：US20220237826A1

公开(公告)日：2022-07-28

申请号：US17658799

申请日：2022-04-11

Applicant: Adobe Inc.

Inventor： Zhihong Ding ,  Scott Cohen ,  Zhe Lin ,  Mingyang Ling

IPC: G06T7/90 ,  G06K9/62 ,  G06N3/02 ,  G06V10/20

Abstract: The present disclosure relates to a color classification system that accurately classifies objects in digital images based on color. In particular, in one or more embodiments, the color classification system utilizes a multidimensional color space and one or more color mappings to match objects to colors. Indeed, the color classification system can accurately and efficiently detect the color of an object utilizing one or more color similarity regions generated in the multidimensional color space.

公开(公告)号：US20210319255A1

公开(公告)日：2021-10-14

申请号：US17331161

申请日：2021-05-26

Applicant: Adobe Inc.

Inventor： Khoi Pham ,  Scott Cohen ,  Zhe Lin ,  Zhihong Ding ,  Walter Wei Tuh Chang

IPC: G06K9/62

Abstract: The present disclosure relates to an object selection system that automatically detects and selects objects in a digital image utilizing a large-scale object detector. For instance, in response to receiving a request to automatically select a query object with an unknown object class in a digital image, the object selection system can utilize a large-scale object detector to detect potential objects in the image, filter out one or more potential objects, and label the remaining potential objects in the image to detect the query object. In some implementations, the large-scale object detector utilizes a region proposal model, a concept mask model, and an auto tagging model to automatically detect objects in the digital image.

公开(公告)号：US11055566B1

公开(公告)日：2021-07-06

申请号：US16817418

申请日：2020-03-12

Applicant: Adobe Inc.

Inventor： Khoi Pham ,  Scott Cohen ,  Zhe Lin ,  Zhihong Ding ,  Walter Wei Tuh Chang

IPC: G06K9/00 ,  G06K9/62

Abstract: The present disclosure relates to an object selection system that automatically detects and selects objects in a digital image utilizing a large-scale object detector. For instance, in response to receiving a request to automatically select a query object with an unknown object class in a digital image, the object selection system can utilize a large-scale object detector to detect potential objects in the image, filter out one or more potential objects, and label the remaining potential objects in the image to detect the query object. In some implementations, the large-scale object detector utilizes a region proposal model, a concept mask model, and an auto tagging model to automatically detect objects in the digital image.

公开(公告)号：US20250095393A1

公开(公告)日：2025-03-20

申请号：US18470778

申请日：2023-09-20

Applicant: ADOBE INC.

Inventor： Ziyan Yang ,  Kushal Kafle ,  Zhe Lin ,  Scott Cohen ,  Zhihong Ding

IPC: G06V20/70 ,  G06F40/205 ,  G06V10/25 ,  G06V10/774

Abstract: A method, apparatus, and non-transitory computer readable medium for image processing are described. Embodiments of the present disclosure obtain an image and an input text including a subject from the image and a location of the subject in the image. An image encoder encodes the image to obtain an image embedding. A text encoder encodes the input text to obtain a text embedding. An image processing apparatus based on the present disclosure generates an output text based on the image embedding and the text embedding. In some examples, the output text includes a relation of the subject to an object from the image and a location of the object in the image.

公开(公告)号：US12136250B2

公开(公告)日：2024-11-05

申请号：US17332734

申请日：2021-05-27

Applicant: Adobe Inc.

Inventor： Khoi Pham ,  Kushal Kafle ,  Zhe Lin ,  Zhihong Ding ,  Scott Cohen ,  Quan Tran

IPC: G06V10/75 ,  G06F18/214 ,  G06F18/25 ,  G06N3/08

Abstract: This disclosure describes one or more implementations of systems, non-transitory computer-readable media, and methods that extract multiple attributes from an object portrayed in a digital image utilizing a multi-attribute contrastive classification neural network. For example, the disclosed systems utilize a multi-attribute contrastive classification neural network that includes an embedding neural network, a localizer neural network, a multi-attention neural network, and a classifier neural network. In some cases, the disclosed systems train the multi-attribute contrastive classification neural network utilizing a multi-attribute, supervised-contrastive loss. In some embodiments, the disclosed systems generate negative attribute training labels for labeled digital images utilizing positive attribute labels that correspond to the labeled digital images.

公开(公告)号：US12045963B2

公开(公告)日：2024-07-23

申请号：US18058630

申请日：2022-11-23

Applicant: Adobe Inc.

Inventor： Scott Cohen ,  Zhe Lin ,  Zhihong Ding ,  Luis Figueroa ,  Kushal Kafle

IPC: G06T5/77 ,  G06F3/04842 ,  G06F3/04845 ,  G06T3/20 ,  G06V10/70 ,  G06V10/86

CPC classification number: G06T5/77 ,  G06F3/04842 ,  G06F3/04845 ,  G06T3/20 ,  G06V10/768 ,  G06V10/86 ,  G06T2200/24 ,  G06T2207/20084 ,  G06T2207/20104

Abstract: The present disclosure relates to systems, methods, and non-transitory computer-readable media that modify digital images via scene-based editing using image understanding facilitated by artificial intelligence. For instance, in one or more embodiments, the disclosed systems detect, via a graphical user interface of a client device, a user selection of an object portrayed within a digital image. The disclosed systems determine, in response to detecting the user selection of the object, a relationship between the object and an additional object portrayed within the digital image. The disclosed systems receive one or more user interactions for modifying the object. The disclosed systems modify the digital image in response to the one or more user interactions by modifying the object and the additional object based on the relationship between the object and the additional object.

公开(公告)号：US20240169685A1

公开(公告)日：2024-05-23

申请号：US18058575

申请日：2022-11-23

Applicant: Adobe Inc.

Inventor： Luis Figueroa ,  Zhe Lin ,  Zhihong Ding ,  Scott Cohen

IPC: G06V10/20 ,  G06F3/04842 ,  G06F3/04845 ,  G06T11/60 ,  G06V10/82

CPC classification number: G06V10/255 ,  G06F3/04842 ,  G06F3/04845 ,  G06T11/60 ,  G06V10/82

Abstract: The present disclosure relates to systems, methods, and non-transitory computer-readable media that modify digital images via scene-based editing using image understanding facilitated by artificial intelligence. For instance, in one or more embodiments, the disclosed systems receive a digital image from a client device. The disclosed systems detect, utilizing a shadow detection neural network, an object portrayed in the digital image. The disclosed systems detect, utilizing the shadow detection neural network, a shadow portrayed in the digital image. The disclosed systems generate, utilizing the shadow detection neural network, an object-shadow pair prediction that associates the shadow with the object.

公开(公告)号：US20240135514A1

公开(公告)日：2024-04-25

申请号：US18460365

申请日：2023-09-01

Applicant: Adobe Inc.

Inventor： Daniil Pakhomov ,  Qing Liu ,  Zhihong Ding ,  Scott Cohen ,  Zhe Lin ,  Jianming Zhang ,  Zhifei Zhang ,  Ohiremen Dibua ,  Mariette Souppe ,  Krishna Kumar Singh ,  Jonathan Brandt

IPC: G06T5/00 ,  G06F3/04845 ,  G06T7/11 ,  G06T7/194 ,  G06T7/70

CPC classification number: G06T5/005 ,  G06F3/04845 ,  G06T5/002 ,  G06T7/11 ,  G06T7/194 ,  G06T7/70 ,  G06T2200/24 ,  G06T2207/20021 ,  G06T2207/20084 ,  G06T2207/20092

Abstract: The present disclosure relates to systems, methods, and non-transitory computer-readable media that modify digital images via multi-layered scene completion techniques facilitated by artificial intelligence. For instance, in some embodiments, the disclosed systems receive a digital image portraying a first object and a second object against a background, where the first object occludes a portion of the second object. Additionally, the disclosed systems pre-process the digital image to generate a first content fill for the portion of the second object occluded by the first object and a second content fill for a portion of the background occluded by the second object. After pre-processing, the disclosed systems detect one or more user interactions to move or delete the first object from the digital image. The disclosed systems further modify the digital image by moving or deleting the first object and exposing the first content fill for the portion of the second object.

公开(公告)号：US11681919B2

公开(公告)日：2023-06-20

申请号：US17331161

申请日：2021-05-26

Applicant: Adobe Inc.

Inventor： Khoi Pham ,  Scott Cohen ,  Zhe Lin ,  Zhihong Ding ,  Walter Wei Tuh Chang

IPC: G06V10/00 ,  G06N3/08 ,  G06F18/2113 ,  G06F18/214 ,  G06F18/21 ,  G06V10/764 ,  G06V10/771 ,  G06V10/774 ,  G06V10/82

CPC classification number: G06N3/08 ,  G06F18/2113 ,  G06F18/2155 ,  G06F18/2163 ,  G06V10/764 ,  G06V10/765 ,  G06V10/771 ,  G06V10/7753 ,  G06V10/82

Abstract: The present disclosure relates to an object selection system that automatically detects and selects objects in a digital image utilizing a large-scale object detector. For instance, in response to receiving a request to automatically select a query object with an unknown object class in a digital image, the object selection system can utilize a large-scale object detector to detect potential objects in the image, filter out one or more potential objects, and label the remaining potential objects in the image to detect the query object. In some implementations, the large-scale object detector utilizes a region proposal model, a concept mask model, and an auto tagging model to automatically detect objects in the digital image.