-
公开(公告)号：US20190197154A1
公开(公告)日：2019-06-27
申请号：US15852506
申请日：2017-12-22
Applicant: Adobe Inc.
Inventor： Scott Cohen , Kushal Kafle , Brian Price
Abstract: Systems and techniques are described that provide for question answering using data visualizations, such as bar graphs. Such data visualizations are often generated from collected data, and provided within image files that illustrate the underlying data and relationships between data elements. The described techniques analyze a query and a related data visualization, and identify one or more spatial regions within the data visualization in which an answer to the query may be found.
-
公开(公告)号：US20190156115A1
公开(公告)日：2019-05-23
申请号：US16251568
申请日：2019-01-18
Applicant: Adobe Inc.
Inventor： Scott Cohen , Brian Lynn Price , Dafang He , Michael F. Kraley , Paul Asente
CPC classification number: G06K9/00456 , G06K9/00463 , G06K9/3233 , G06K9/342 , G06K9/4628 , G06K9/6256 , G06K9/627 , G06N3/0454 , G06N3/08
Abstract: Disclosed systems and methods generate page segmented documents from unstructured vector graphics documents. The page segmentation application executing on a computing device receives as input an unstructured vector graphics document. The application generates an element proposal for each of many areas on a page of the input document tentatively identified as being page elements. The page segmentation application classifies each of the element proposals into one of a plurality of defined type of categories of page elements. The page segmentation application may further refine at least one of the element proposals and select a final element proposal for each element within the unstructured vector document. One or more of the page segmentation steps may be performed using a neural network.
-
公开(公告)号：US20190108414A1
公开(公告)日：2019-04-11
申请号：US16216739
申请日：2018-12-11
Applicant: Adobe Inc.
Inventor： Brian Price , Scott Cohen , Ning Xu
Abstract: Systems and methods are disclosed for selecting target objects within digital images. In particular, in one or more embodiments, the disclosed systems and methods generate a trained neural network based on training digital images and training indicators. Moreover, one or more embodiments of the disclosed systems and methods utilize a trained neural network and iterative user indicators to select targeted objects in digital images. Specifically, the disclosed systems and methods can transform user indicators into distance maps that can be utilized in conjunction with color channels and a trained neural network to identify pixels that reflect the target object.
-
公开(公告)号：US12204610B2
公开(公告)日：2025-01-21
申请号：US17650967
申请日：2022-02-14
Applicant: Adobe Inc.
Inventor： Zhe Lin , Haitian Zheng , Jingwan Lu , Scott Cohen , Jianming Zhang , Ning Xu , Elya Shechtman , Connelly Barnes , Sohrab Amirghodsi
IPC: G06K9/00 , G06F18/214 , G06N3/08 , G06T5/77 , G06T7/11
Abstract: The present disclosure relates to systems, methods, and non-transitory computer readable media for training a generative inpainting neural network to accurately generate inpainted digital images via object-aware training and/or masked regularization. For example, the disclosed systems utilize an object-aware training technique to learn parameters for a generative inpainting neural network based on masking individual object instances depicted within sample digital images of a training dataset. In some embodiments, the disclosed systems also (or alternatively) utilize a masked regularization technique as part of training to prevent overfitting by penalizing a discriminator neural network utilizing a regularization term that is based on an object mask. In certain cases, the disclosed systems further generate an inpainted digital image utilizing a trained generative inpainting model with parameters learned via the object-aware training and/or the masked regularization.
-
公开(公告)号：US20240171848A1
公开(公告)日：2024-05-23
申请号：US18058554
申请日：2022-11-23
Applicant: Adobe Inc.
Inventor： Luis Figueroa , Zhihong Ding , Scott Cohen , Zhe Lin , Qing Liu
CPC classification number: H04N23/632 , G06V10/273 , G06V10/764 , G06V10/82 , G06V10/945 , H04N5/2628
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 provide, for display within a graphical user interface of a client device, a digital image displaying a plurality of objects, the plurality of objects comprising a plurality of different types of objects. The disclosed systems generate, utilizing a segmentation neural network and without user input, an object mask for objects of the plurality of objects. The disclosed systems determine, utilizing a distractor detection neural network, a classification for the objects of the plurality of objects. The disclosed systems remove at least one object from the digital image, based on classifying the at least one object as a distracting object, by deleting the object mask for the at least one object.
-
Patent Agency Ranking
46.发明公开公开(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：US20240135613A1
公开(公告)日：2024-04-25
申请号：US18320664
申请日：2023-05-19
Applicant: Adobe Inc.
Inventor： Zhihong Ding , Scott Cohen , Matthew Joss , Jianming Zhang , Darshan Prasad , Celso Gomes , Jonathan Brandt
CPC classification number: G06T11/60 , G06F3/04842 , G06T3/40 , G06T5/005 , G06T7/50 , G06V10/761 , G06T2207/20084
Abstract: The present disclosure relates to systems, methods, and non-transitory computer-readable media that implement perspective-aware object move operations for digital image editing. For instance, in some embodiments, the disclosed systems determine a vanishing point associated with a digital image portraying an object. Additionally, the disclosed systems detect one or more user interactions for moving the object within the digital image. Based on moving the object with respect to the vanishing point, the disclosed systems perform a perspective-based resizing of the object within the digital image.
-
公开(公告)号：US20230360180A1
公开(公告)日：2023-11-09
申请号：US17661985
申请日：2022-05-04
Applicant: Adobe Inc.
Inventor： Haitian Zheng , Zhe Lin , Jingwan Lu , Scott Cohen , Elya Shechtman , Connelly Barnes , Jianming Zhang , Ning Xu , Sohrab Amirghodsi
CPC classification number: G06T5/005 , G06T3/4046 , G06V10/40 , G06T2207/20084
Abstract: The present disclosure relates to systems, methods, and non-transitory computer readable media that generate inpainted digital images utilizing a cascaded modulation inpainting neural network. For example, the disclosed systems utilize a cascaded modulation inpainting neural network that includes cascaded modulation decoder layers. For example, in one or more decoder layers, the disclosed systems start with global code modulation that captures the global-range image structures followed by an additional modulation that refines the global predictions. Accordingly, in one or more implementations, the image inpainting system provides a mechanism to correct distorted local details. Furthermore, in one or more implementations, the image inpainting system leverages fast Fourier convolutions block within different resolution layers of the encoder architecture to expand the receptive field of the encoder and to allow the network encoder to better capture global structure.
-
49.发明公开公开(公告)号：US20230325996A1
公开(公告)日：2023-10-12
申请号：US18167690
申请日：2023-02-10
Applicant: Adobe Inc.
Inventor： Zhifei Zhang , Jianming Zhang , Scott Cohen , Zhe Lin
IPC: G06T5/50 , G06T3/40 , G06V10/60 , G06F3/04842
CPC classification number: G06T5/50 , G06T3/40 , G06V10/60 , G06F3/04842 , G06T2207/20101 , G06T2207/20104 , G06T2207/20221
Abstract: The present disclosure relates to systems, methods, and non-transitory computer readable media that generates composite images via auto-compositing features. For example, in one or more embodiments, the disclosed systems determine a background image and a foreground object image for use in generating a composite image. The disclosed systems further provide, for display within a graphical user interface of a client device, at least one selectable option for executing an auto-composite model for the composite image, the auto-composite model comprising at least one of a scale prediction model, a harmonization model, or a shadow generation model. The disclosed systems detect, via the graphical user interface, a user selection of the at least one selectable option and generate, in response to detecting the user selection, the composite image by executing the auto-composite model using the background image and the foreground object image.
-
50.发明公开公开(公告)号：US20230325991A1
公开(公告)日：2023-10-12
申请号：US17658770
申请日：2022-04-11
Applicant: Adobe Inc.
Inventor： Zhe Lin , Sijie Zhu , Jason Wen Yong Kuen , Scott Cohen , Zhifei Zhang
CPC classification number: G06T5/50 , G06T7/194 , G06T5/002 , G06T3/60 , G06T2207/20084 , G06T2207/20221
Abstract: The present disclosure relates to systems, methods, and non-transitory computer readable media that utilizes artificial intelligence to learn to recommend foreground object images for use in generating composite images based on geometry and/or lighting features. For instance, in one or more embodiments, the disclosed systems transform a foreground object image corresponding to a background image using at least one of a geometry transformation or a lighting transformation. The disclosed systems further generating predicted embeddings for the background image, the foreground object image, and the transformed foreground object image within a geometry-lighting-sensitive embedding space utilizing a geometry-lighting-aware neural network. Using a loss determined from the predicted embeddings, the disclosed systems update parameters of the geometry-lighting-aware neural network. The disclosed systems further provide a variety of efficient user interfaces for generating composite digital images.
-
-
-
-
-
-
-
-
-
Search Results
115
records
(took 0.02 seconds)
