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公开(公告)号：US20250078349A1
公开(公告)日：2025-03-06
申请号：US18459526
申请日：2023-09-01
Applicant: ADOBE INC.
Inventor： Wonwoong Cho , Hareesh Ravi , Midhun Harikumar , Vinh Ngoc Khuc , Krishna Kumar Singh , Jingwan Lu , Ajinkya Gorakhnath Kale
Abstract: A method, apparatus, and non-transitory computer readable medium for image generation are described. Embodiments of the present disclosure obtain a content input and a style input via a user interface or from a database. The content input includes a target spatial layout and the style input includes a target style. A content encoder of an image processing apparatus encodes the content input to obtain a spatial layout mask representing the target spatial layout. A style encoder of the image processing apparatus encodes the style input to obtain a style embedding representing the target style. An image generation model of the image processing apparatus generates an image based on the spatial layout mask and the style embedding, where the image includes the target spatial layout and the target style.
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公开(公告)号：US20230252071A1
公开(公告)日：2023-08-10
申请号：US18302201
申请日：2023-04-18
Applicant: Adobe Inc.
Inventor： Pramod Srinivasan , Zhe Lin , Samarth Gulati , Saeid Motiian , Midhun Harikumar , Baldo Antonio Faieta , Alex C. Filipkowski
IPC: G06F16/532 , G06F16/538 , G06F40/30 , G06F16/583 , G06F16/51 , G06F16/54
CPC classification number: G06F16/532 , G06F16/51 , G06F16/54 , G06F16/538 , G06F16/583 , G06F40/30
Abstract: Keyword localization digital image search techniques are described. These techniques support an ability to indicate “where” a corresponding keyword is to be expressed with respect to a layout in a respective digital image resulting from a search query. The search query may also include an indication of a size of the keyword as expressed in the digital image, a number of instances of the keyword, and so forth. Additionally, the techniques and systems as described herein support real time search through use of keyword signatures.
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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
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.
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公开(公告)号：US11138257B2
公开(公告)日：2021-10-05
申请号：US16745143
申请日：2020-01-16
Applicant: Adobe Inc.
Inventor： Midhun Harikumar , Zhe Lin , Pramod Srinivasan , Jianming Zhang , Daniel David Miranda , Baldo Antonio Faieta
IPC: G06F16/532 , G06F3/0484 , G06T7/11 , G06F16/538 , G06F16/587 , G06T7/70
Abstract: Object search techniques for digital images are described. In the techniques described herein, semantic features are extracted on a per-object basis form a digital image. This supports location of objects within digital images and is not limited to semantic features of an entirety of the digital image as involved in conventional image similarity search techniques. This may be combined with indications a location of the object globally with respect to the digital image through use of a global segmentation mask, use of a local segmentation mask to capture post and characteristics of the object itself, and so on.
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公开(公告)号：US20210224312A1
公开(公告)日：2021-07-22
申请号：US16745143
申请日：2020-01-16
Applicant: Adobe Inc.
Inventor： Midhun Harikumar , Zhe Lin , Pramod Srinivasan , Jianming Zhang , Daniel David Miranda , Baldo Antonio Faieta
IPC: G06F16/532 , G06F3/0484 , G06T7/11 , G06T7/70 , G06F16/538 , G06F16/587
Abstract: Object search techniques for digital images are described. In the techniques described herein, semantic features are extracted on a per-object basis form a digital image. This supports location of objects within digital images and is not limited to semantic features of an entirety of the digital image as involved in conventional image similarity search techniques. This may be combined with indications a location of the object globally with respect to the digital image through use of a global segmentation mask, use of a local segmentation mask to capture post and characteristics of the object itself, and so on.
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Patent Agency Ranking
公开(公告)号：US12277630B2
公开(公告)日：2025-04-15
申请号：US17662560
申请日：2022-05-09
Applicant: ADOBE INC.
Inventor： Pranav Vineet Aggarwal , Midhun Harikumar , Ajinkya Gorakhnath Kale
Abstract: Systems and methods for image processing are configured. Embodiments of the present disclosure identify target style attributes and target structure attributes for a composite image; generate a matrix of composite feature tokens based on the target style attributes and the target structure attributes, wherein subsequent feature tokens of the matrix of composite feature tokens are sequentially generated based on previous feature tokens of the matrix of composite feature tokens according to a linear ordering of the matrix of composite feature tokens; and generate the composite image based on the matrix of composite feature tokens, wherein the composite image includes the target style attributes and the target structure attributes.
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公开(公告)号：US20250117974A1
公开(公告)日：2025-04-10
申请号：US18908075
申请日：2024-10-07
Applicant: ADOBE INC.
Inventor： Midhun Harikumar , Nicholas Isaac Kolkin , Sachin Madhav Kelkar , Purvak Lapsiya , Elya Shechtman , Ajinkya Gorakhnath Kale , Jalansh Saumil Munshi
IPC: G06T11/00 , G06F40/284 , G06T5/70
Abstract: A method, apparatus, non-transitory computer readable medium, and system for generating synthetic images depicting an image element with a target composition include obtaining a content input and a composition input. The content input indicates an image element and the composition input indicates a target composition of the image element. Embodiments then encode the composition input to obtain a composition embedding representing the target composition. Subsequently, embodiments generate, using an image generation model, a synthetic image based on the content input and the composition embedding. The synthetic image depicts the image element with the target composition.
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公开(公告)号：US12008698B2
公开(公告)日：2024-06-11
申请号：US18117155
申请日：2023-03-03
Applicant: Adobe Inc.
Inventor： Midhun Harikumar , Pranav Aggarwal , Baldo Faieta , Ajinkya Kale , Zhe Lin
CPC classification number: G06T11/60 , G06T7/11 , G06T7/162 , G06T2207/20081 , G06T2207/20084
Abstract: A non-transitory computer-readable medium includes program code that is stored thereon. The program code is executable by one or more processing devices for performing operations including generating, using a model, a learned image representation of a target image. The operations further include generating, using a text embedding model, a text embedding of a text query. The text embedding and the learned image representation of the target image are in a same embedding space. Additionally, the operations include convolving the learned image representation of the target image with the text embedding of the text query. Moreover, the operations include generating an object-segmented image based on the convolving of the learned image representation of the target image with the text embedding.
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公开(公告)号：US20240020954A1
公开(公告)日：2024-01-18
申请号：US17812596
申请日：2022-07-14
Applicant: ADOBE INC.
Inventor： Sachin Kelkar , Ajinkya Gorakhnath Kale , Midhun Harikumar
IPC: G06V10/774 , G06T5/00 , G06T7/194 , G06V10/771 , G06V10/776 , G06V10/26 , G06V10/75 , G06F16/532
CPC classification number: G06V10/774 , G06T5/005 , G06T7/194 , G06V10/771 , G06V10/776 , G06V10/267 , G06V10/759 , G06F16/532 , G06T2207/20081 , G06V2201/10
Abstract: Systems and methods for image processing, and specifically for generating object-agnostic image representations, are described. Embodiments of the present disclosure receive a training image including a foreground object and a background, remove the foreground object from the training image to obtain a modified training image, inpaint a portion of the modified training image corresponding to the foreground object to obtain an inpainted training image, encode the training image and the inpainted training image using a machine learning model to obtain an encoded training image and an encoded inpainted training image, and update parameters of the machine learning model based on the encoded training image and the encoded inpainted training image.
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公开(公告)号：US20230360294A1
公开(公告)日：2023-11-09
申请号：US17662560
申请日：2022-05-09
Applicant: ADOBE INC.
Inventor： Pranav Vineet Aggarwal , Midhun Harikumar , Ajinkya Gorakhnath Kale
CPC classification number: G06T11/40 , G06N3/0454 , G06N3/088 , G06T7/13 , G06T2207/20081 , G06T2207/20084
Abstract: Systems and methods for image processing are configured. Embodiments of the present disclosure identify target style attributes and target structure attributes for a composite image; generate a matrix of composite feature tokens based on the target style attributes and the target structure attributes, wherein subsequent feature tokens of the matrix of composite feature tokens are sequentially generated based on previous feature tokens of the matrix of composite feature tokens according to a linear ordering of the matrix of composite feature tokens; and generate the composite image based on the matrix of composite feature tokens, wherein the composite image includes the target style attributes and the target structure attributes.
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