公开(公告)号：US20240320873A1

公开(公告)日：2024-09-26

申请号：US18439036

申请日：2024-02-12

Applicant: ADOBE INC.

Inventor： Tobias Hinz ,  Ali Aminian ,  Hao Tan ,  Kushal Kafle ,  Oliver Wang ,  Jingwan Lu

IPC: G06T11/00

CPC classification number: G06T11/00 ,  G06T2211/441

Abstract: A method, apparatus, non-transitory computer readable medium, and system for image generation include obtaining a text prompt and encoding, using a text encoder jointly trained with an image generation model, the text prompt to obtain a text embedding. Some embodiments generate, using the image generation model, a synthetic image based on the text embedding.

公开(公告)号：US20240168617A1

公开(公告)日：2024-05-23

申请号：US18058622

申请日：2022-11-23

Applicant: Adobe Inc.

Inventor： Zhe Lin ,  Scott Cohen ,  Kushal Kafle

IPC: G06F3/04845 ,  G06F3/0482 ,  G06F3/04847 ,  G06F3/04886 ,  G06F40/166 ,  G06V10/26 ,  G06V20/70

CPC classification number: G06F3/04845 ,  G06F3/0482 ,  G06F3/04847 ,  G06F3/04886 ,  G06F40/166 ,  G06V10/26 ,  G06V20/70 ,  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 detect a selection of an object portrayed in a digital image displayed within a graphical user interface of a client device. The disclosed systems provide, for display within the graphical user interface in response to detecting the selection of the object, an interactive window displaying one or more attributes of the object. The disclosed systems receive, via the interactive window, a user interaction to change an attribute from the one or more attributes. The disclosed systems modify the digital image by changing the attribute of the object in accordance with the user interaction.

公开(公告)号：US11967128B2

公开(公告)日：2024-04-23

申请号：US17333583

申请日：2021-05-28

Applicant: ADOBE INC.

Inventor： Qiuyu Chen ,  Quan Hung Tran ,  Kushal Kafle ,  Trung Huu Bui ,  Franck Dernoncourt ,  Walter Chang

IPC: G06V10/56 ,  G06F16/51 ,  G06F16/532 ,  G06F16/56 ,  G06F16/583 ,  G06V10/25 ,  G06V10/774 ,  G06V10/82

CPC classification number: G06V10/56 ,  G06F16/51 ,  G06F16/532 ,  G06F16/56 ,  G06F16/5838 ,  G06V10/25 ,  G06V10/774 ,  G06V10/82 ,  G06T2207/20081

Abstract: The present disclosure describes a model for large scale color prediction of objects identified in images. Embodiments of the present disclosure include an object detection network, an attention network, and a color classification network. The object detection network generates object features for an object in an image and may include a convolutional neural network (CNN), region proposal network, or a ResNet. The attention network generates an attention vector for the object based on the object features, wherein the attention network takes a query vector based on the object features, and a plurality of key vector and a plurality of value vectors corresponding to a plurality of colors as input. The color classification network generates a color attribute vector based on the attention vector, wherein the color attribute vector indicates a probability of the object including each of the plurality of colors.

公开(公告)号：US20220383031A1

公开(公告)日：2022-12-01

申请号：US17333583

申请日：2021-05-28

Applicant: Adobe INC.

Inventor： Qiuyu Chen ,  Quan Hung Tran ,  Kushal Kafle ,  Trung Huu Bui ,  Franck Dernoncourt ,  Walter Chang

IPC: G06K9/46 ,  G06K9/32 ,  G06F16/51 ,  G06F16/583 ,  G06F16/532 ,  G06F16/56

Abstract: The present disclosure describes a model for large scale color prediction of objects identified in images. Embodiments of the present disclosure include an object detection network, an attention network, and a color classification network. The object detection network generates object features for an object in an image and may include a convolutional neural network (CNN), region proposal network, or a ResNet. The attention network generates an attention vector for the object based on the object features, wherein the attention network takes a query vector based on the object features, and a plurality of key vector and a plurality of value vectors corresponding to a plurality of colors as input. The color classification network generates a color attribute vector based on the attention vector, wherein the color attribute vector indicates a probability of the object including each of the plurality of colors.

公开(公告)号：US20240169502A1

公开(公告)日：2024-05-23

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: G06T5/00 ,  G06F3/04842 ,  G06F3/04845 ,  G06T3/20 ,  G06V10/70 ,  G06V10/86

CPC classification number: G06T5/005 ,  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：US20240037906A1

公开(公告)日：2024-02-01

申请号：US17814921

申请日：2022-07-26

Applicant: ADOBE INC.

Inventor： Qiuyu Chen ,  Quan Hung Tran ,  Kushal Kafle ,  Trung Huu Bui ,  Franck Dernoncourt ,  Walter W. Chang

IPC: G06V10/764 ,  G06V10/56 ,  G06V10/774

CPC classification number: G06V10/764 ,  G06V10/56 ,  G06V10/774 ,  G06V2201/10

Abstract: Systems and methods for color prediction are described. Embodiments of the present disclosure receive an image that includes an object including a color, generate a color vector based on the image using a color classification network, where the color vector includes a color value corresponding to each of a set of colors, generate a bias vector by comparing the color vector to teach of a set of center vectors, where each of the set of center vectors corresponds to a color of the set of colors, and generate an unbiased color vector based on the color vector and the bias vector, where the unbiased color vector indicates the color of the object.

公开(公告)号：US20220383037A1

公开(公告)日：2022-12-01

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: G06K9/62 ,  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：US20250022252A1

公开(公告)日：2025-01-16

申请号：US18899571

申请日：2024-09-27

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：US20240046412A1

公开(公告)日：2024-02-08

申请号：US17880120

申请日：2022-08-03

Applicant: ADOBE INC.

Inventor： Md Mehrab Tanjim ,  Krishna Kumar Singh ,  Kushal Kafle ,  Ritwik Sinha

IPC: G06T3/40

CPC classification number: G06T3/4046 ,  G06T3/4053

Abstract: A system debiases image translation models to produce generated images that contain minority attributes. A balanced batch for a minority attribute is created by over-sampling images having the minority attribute from an image dataset. An image translation model is trained using images from the balanced batch by applying supervised contrastive loss to output of an encoder of the image translation model and an auxiliary classifier loss based on predicted attributes in images generated by a decoder of the image translation model. Once trained, the image translation model is used to generate images with the minority image when given an input image having the minority attribute.

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.