公开(公告)号：US11710312B2

公开(公告)日：2023-07-25

申请号：US17865076

申请日：2022-07-14

Applicant: Adobe Inc.

Inventor： Jayant Kumar ,  Vera Lychagina ,  Tarun Vashisth ,  Sudhakar Pandey ,  Sharad Mangalick ,  Rohith Mohan Dodle ,  Peter Baust ,  Mina Doroudi ,  Kerem Turgutlu ,  Kannan Iyer ,  Gaurav Kukal ,  Archit Kalra ,  Amine Ben Khalifa

IPC: G06V20/00 ,  G06F16/53 ,  G06N3/08 ,  G06F18/23 ,  G06F18/214

CPC classification number: G06V20/35 ,  G06F16/53 ,  G06F18/214 ,  G06F18/23 ,  G06N3/08

Abstract: Disclosed are systems and methods for dynamically determining categories for images. A computer-implemented method may include training a neural network to receive an input image and determine one or more image categories associated with the input image; obtaining a set of images associated with a user; determining, using the trained neural network, one or more image categories associated with each image included in the obtained set of images; determining one or more dominant image categories associated with the user based on the determined image categories for the obtained set of images; and determining an image editing user interface for the user based on the determined one or more dominant image categories.

公开(公告)号：US11645095B2

公开(公告)日：2023-05-09

申请号：US17475145

申请日：2021-09-14

Applicant: Adobe Inc.

Inventor： Jayant Kumar ,  Manasi Deshmukh ,  Ming Liu ,  Ashok Gupta ,  Karthik Suresh ,  Chirag Arora ,  Jing Zheng ,  Ravindra Sadaphule ,  Vipul Dalal ,  Andrei Stefan

IPC: G06F9/451 ,  G06F40/20 ,  G06N5/022 ,  G06T11/60

CPC classification number: G06F9/453 ,  G06F40/20 ,  G06N5/022 ,  G06T11/60

Abstract: This disclosure describes methods, non-transitory computer readable storage media, and systems that generate a digital knowledge graph based on a plurality of tutorial content items to generate recommendations of digital resource items. Specifically, the disclosed system extracts a plurality of tasks, subject categories related to the tasks, and context signals related to an environment for the tasks from a plurality of tutorial content items for one or more digital content editing applications. The disclosed system generates a digital knowledge graph including nodes corresponding to the tasks and subject categories connected via edges based on relationships extracted from the tutorial content items. In some embodiments, the disclosed system also includes nodes corresponding to digital resource items in the digital knowledge graph or in a subgraph. The disclosed system utilizes the digital knowledge graph with context data to provide a recommendation of digital resource items for display at a client device.

公开(公告)号：US11238593B2

公开(公告)日：2022-02-01

申请号：US16789088

申请日：2020-02-12

Applicant: Adobe Inc.

Inventor： Kerem Can Turgutlu ,  Jayant Kumar ,  Jianming Zhang ,  Zhe Lin

IPC: G06T7/00 ,  G06T7/11 ,  G06T3/40 ,  G06T7/194 ,  G06N3/04

Abstract: Techniques are disclosed for parsing a source image, to identify segments of one or more objects within the source image. The parsing is carried out by an image parsing pipeline that includes three distinct stages comprising three respectively neural network models. The source image can include one or more objects. A first neural network model of the pipeline identifies a section of the source image that includes the object comprising a plurality of segments. A second neural network model of the pipeline generates, from the section of the source image, a mask image, where the mask image identifies one or more segments of the object. A third neural network model of the pipeline further refines the identification of the segments in the mask image, to generate a parsed image. The parsed image identifies the segments of the object, by assigning corresponding unique labels to pixels of different segments of the object.

公开(公告)号：US10853700B2

公开(公告)日：2020-12-01

申请号：US16928949

申请日：2020-07-14

Applicant: Adobe Inc.

Inventor： Jayant Kumar ,  Zhe Lin ,  Vipulkumar C. Dalal

IPC: G06K9/62

Abstract: There is described a computing device and method in a digital medium environment for custom auto tagging of multiple objects. The computing device includes an object detection network and multiple image classification networks. An image is received at the object detection network and includes multiple visual objects. First feature maps are applied to the image at the object detection network and generate object regions associated with the visual objects. The object regions are assigned to the multiple image classification networks, and each image classification network is assigned to a particular object region. The second feature maps are applied to each object region at each image classification network, and each image classification network outputs one or more classes associated with a visual object corresponding to each object region.

公开(公告)号：US11468674B2

公开(公告)日：2022-10-11

申请号：US16995869

申请日：2020-08-18

Applicant: Adobe Inc.

Inventor： Jayant Kumar ,  Vera Lychagina ,  Tarun Vashisth ,  Sudhakar Pandey ,  Sharad Mangalick ,  Rohith Mohan Dodle ,  Peter Baust ,  Mina Doroudi ,  Kerem Turgutlu ,  Kannan Iyer ,  Gaurav Kukal ,  Archit Kalra ,  Amine Ben Khalifa

IPC: G06V20/00 ,  G06K9/62 ,  G06F16/53 ,  G06N3/08

Abstract: Disclosed are systems and methods for dynamically determining categories for images. A computer-implemented method includes training a neural network to receive an input image and determine one or more image categories associated with the input image; obtaining a set of images associated with a user; and determining, using the trained neural network, one or more image categories associated with each image included in the obtained set of images.

公开(公告)号：US20210248748A1

公开(公告)日：2021-08-12

申请号：US16789088

申请日：2020-02-12

Applicant: Adobe Inc.

Inventor： Kerem Can Turgutlu ,  Jayant Kumar ,  Jianming Zhang ,  Zhe Lin

IPC: G06T7/11 ,  G06T3/40 ,  G06T7/194 ,  G06N3/04

Abstract: Techniques are disclosed for parsing a source image, to identify segments of one or more objects within the source image. The parsing is carried out by an image parsing pipeline that includes three distinct stages comprising three respectively neural network models. The source image can include one or more objects. A first neural network model of the pipeline identifies a section of the source image that includes the object comprising a plurality of segments. A second neural network model of the pipeline generates, from the section of the source image, a mask image, where the mask image identifys one or more segments of the object. A third neural network model of the pipeline further refines the identification of the segments in the mask image, to generate a parsed image. The parsed image identifies the segments of the object, by assigning corresponding unique labels to pixels of different segments of the object.

公开(公告)号：US10733480B2

公开(公告)日：2020-08-04

申请号：US16039311

申请日：2018-07-18

Applicant: Adobe Inc.

Inventor： Jayant Kumar ,  Zhe Lin ,  Vipulkumar C. Dalal

IPC: G06K9/62

Abstract: There is described a computing device and method in a digital medium environment for custom auto tagging of multiple objects. The computing device includes an object detection network and multiple image classification networks. An image is received at the object detection network and includes multiple visual objects. First feature maps are applied to the image at the object detection network and generate object regions associated with the visual objects. The object regions are assigned to the multiple image classification networks, and each image classification network is assigned to a particular object region. The second feature maps are applied to each object region at each image classification network, and each image classification network outputs one or more classes associated with a visual object corresponding to each object region.

公开(公告)号：US11775734B2

公开(公告)日：2023-10-03

申请号：US17534937

申请日：2021-11-24

Applicant: Adobe Inc.

Inventor： Sanat Sharma ,  Jing Zheng ,  Jayant Kumar

IPC: G06F40/109 ,  G06N3/02 ,  G06N5/02

CPC classification number: G06F40/109 ,  G06N3/02 ,  G06N5/02

Abstract: Embodiments are disclosed for receiving a modal input including at least one of a text input or an image input. The method may include extracting an intent label from the modal input. The method may further include generating, by an intent embedding generator, an intent embedding from the intent label. The method may further include comparing the intent embedding to a plurality of candidate font embeddings to obtain one or more candidate fonts based on a similarity of the intent embedding to the plurality of candidate font embeddings in an embedding space. The method may further include identifying a recommended font based on the similarity of the intent embedding to a selected candidate font embedding of the plurality of candidate font embeddings.

公开(公告)号：US20230237251A1

公开(公告)日：2023-07-27

申请号：US17583818

申请日：2022-01-25

Applicant: Adobe Inc.

Inventor： Oliver Brdiczka ,  Sanat Sharma ,  Jayant Kumar ,  Alexandru Vasile Costin ,  Aliakbar Darabi ,  Kushith Amerasinghe

IPC: G06F40/166 ,  G06F40/106 ,  G06V30/413 ,  G06F16/58 ,  G06F16/38

CPC classification number: G06F40/166 ,  G06F40/106 ,  G06V30/413 ,  G06F16/5866 ,  G06F16/38

Abstract: An illustrator system accesses a multi-element document, the multi-element document including a plurality of elements. The illustrator system determines, for each of the plurality of elements, an element-specific topic distribution comprising a ranked list of topics. The illustrator system creates a first aggregated topic distribution from the determined element-specific topic distributions. The illustrator system determines a global intent for the multi-element document, the global intent including one or more terms from the first aggregated topic distribution. The illustrator system queries a database using the global intent to retrieve a substitute element. The illustrator system generates a replacement multi-element document that includes a substitute element in place of an element in the multi-element document The at least one substitute element is different from the element in the displayed multi-element document.