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11.发明公开公开(公告)号：US20230259587A1
公开(公告)日：2023-08-17
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
CPC classification number: G06K9/6257 , G06T5/005 , G06T7/11 , G06N3/08 , G06T2207/20084 , G06T2207/20081
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
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公开(公告)号：US20230237088A1
公开(公告)日：2023-07-27
申请号：US18191651
申请日：2023-03-28
Applicant: Adobe Inc.
Inventor： Scott Cohen , Zhe Lin , Mingyang Ling
IPC: G06F16/535 , G06V10/20 , G06F18/24 , G06F18/2113 , G06V10/764 , G06V10/82 , G06V20/70 , G06V20/10
CPC classification number: G06F16/535 , G06V10/255 , G06F18/24 , G06F18/2113 , G06V10/764 , G06V10/82 , G06V20/70 , G06V20/10
Abstract: The present disclosure relates to an object selection system that accurately detects and optionally automatically selects user-requested objects (e.g., query objects) in digital images. For example, the object selection system builds and utilizes an object selection pipeline to determine which object detection neural network to utilize to detect a query object based on analyzing the object class of a query object. In particular, the object selection system can identify both known object classes as well as objects corresponding to unknown object classes.
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公开(公告)号：US11551384B2
公开(公告)日：2023-01-10
申请号：US17323086
申请日：2021-05-18
Applicant: Adobe Inc.
Inventor： Ankit Phogat , Vineet Batra , Sayan Ghosh , Stephen DiVerdi , Scott Cohen
Abstract: Certain embodiments involve flow-based color transfers from a source graphic to target graphic. For instance, a palette flow is computed that maps colors of a target color palette to colors of the source color palette (e.g., by minimizing an earth-mover distance with respect to the source and target color palettes). In some embodiments, such color palettes are extracted from vector graphics using path and shape data. To modify the target graphic, the target color from the target graphic is mapped, via the palette flow, to a modified target color using color information of the source color palette. A modification to the target graphic is performed (e.g., responsive to a preview function or recoloring command) by recoloring an object in the target color with the modified target color.
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公开(公告)号：US20220207745A1
公开(公告)日：2022-06-30
申请号：US17655493
申请日：2022-03-18
Applicant: Adobe Inc.
Inventor： Scott Cohen , Long Mai , Jun Hao Liew , Brian Price
Abstract: The present disclosure relates to systems, non-transitory computer-readable media, and methods for training and utilizing scale-diverse segmentation neural networks to analyze digital images at different scales and identify different target objects portrayed in the digital images. For example, in one or more embodiments, the disclosed systems analyze a digital image and corresponding user indicators (e.g., foreground indicators, background indicators, edge indicators, boundary region indicators, and/or voice indicators) at different scales utilizing a scale-diverse segmentation neural network. In particular, the disclosed systems can utilize the scale-diverse segmentation neural network to generate a plurality of semantically meaningful object segmentation outputs. Furthermore, the disclosed systems can provide the plurality of object segmentation outputs for display and selection to improve the efficiency and accuracy of identifying target objects and modifying the digital image.
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公开(公告)号：US11043012B2
公开(公告)日：2021-06-22
申请号：US16533308
申请日：2019-08-06
Applicant: Adobe Inc.
Inventor： Ankit Phogat , Vineet Batra , Sayan Ghosh , Stephen DiVerdi , Scott Cohen
Abstract: Certain embodiments involve flow-based color transfers from a source graphic to target graphic. For instance, a palette flow is computed that maps colors of a target color palette to colors of the source color palette (e.g., by minimizing an earth-mover distance with respect to the source and target color palettes). In some embodiments, such color palettes are extracted from vector graphics using path and shape data. To modify the target graphic, the target color from the target graphic is mapped, via the palette flow, to a modified target color using color information of the source color palette. A modification to the target graphic is performed (e.g., responsive to a preview function or recoloring command) by recoloring an object in the target color with the modified target color.
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Patent Agency Ranking
公开(公告)号：US20210081766A1
公开(公告)日：2021-03-18
申请号：US16573342
申请日：2019-09-17
Applicant: Adobe Inc.
Inventor： Scott Cohen , Curtis Wigington , Brian Price
Abstract: Described techniques for multi-label classification, in which sequential data includes characters that have two or more aspects that require classification, are capable of providing separate classifications for different categories of components. Using an appropriately-trained neural network, the described techniques perform aligning and otherwise combining two or more classifications (e.g., categories, or types of labels) to obtain multi-label characters.
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公开(公告)号：US20200034971A1
公开(公告)日：2020-01-30
申请号：US16047492
申请日：2018-07-27
Applicant: Adobe Inc.
Inventor： Ning Xu , Brian Price , Scott Cohen
Abstract: A temporal object segmentation system determines a location of an object depicted in a video. In some cases, the temporal object segmentation system determines the object's location in a particular frame of the video based on information indicating a previous location of the object in a previous video frame. For example, an encoder neural network in the temporal object segmentation system extracts features describing image attributes of a video frame. A convolutional long-short term memory neural network determines the location of the object in the frame, based on the extracted image attributes and information indicating a previous location in a previous frame. A decoder neural network generates an image mask indicating the object's location in the frame. In some cases, a video editing system receives multiple generated masks for a video, and modifies one or more video frames based on the locations indicated by the masks.
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公开(公告)号：US10475207B2
公开(公告)日：2019-11-12
申请号：US16057161
申请日：2018-08-07
Applicant: Adobe Inc.
Inventor： Jimei Yang , Yu-Wei Chao , Scott Cohen , Brian Price
Abstract: A forecasting neural network receives data and extracts features from the data. A recurrent neural network included in the forecasting neural network provides forecasted features based on the extracted features. In an embodiment, the forecasting neural network receives an image, and features of the image are extracted. The recurrent neural network forecasts features based on the extracted features, and pose is forecasted based on the forecasted features. Additionally or alternatively, additional poses are forecasted based on additional forecasted features.
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公开(公告)号：US10424064B2
公开(公告)日：2019-09-24
申请号：US15296845
申请日：2016-10-18
Applicant: Adobe Inc.
Inventor： Brian Price , Scott Cohen , Jimei Yang
Abstract: Certain aspects involve semantic segmentation of objects in a digital visual medium by determining a score for each pixel of the digital visual medium that is representative of a likelihood that each pixel corresponds to the objects associated with bounding boxes within the digital visual medium. An instance-level label that yields a label for each of the pixels of the digital visual medium corresponding to the objects is determined based, in part, on a collective probability map including the score for each pixel of the digital visual medium. In some aspects, the score for each pixel corresponding to each bounding box is determined by a prediction model trained by a neural network.
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20.发明申请公开(公告)号：US20250054116A1
公开(公告)日：2025-02-13
申请号：US18929330
申请日：2024-10-28
Applicant: Adobe Inc.
Inventor： Haitian Zheng , Zhe Lin , Jingwan Lu , Scott Cohen , Elya Shechtman , Connelly Barnes , Jianming Zhang , Ning Xu , Sohrab Amirghodsi
IPC: G06T5/77 , G06T3/4046 , G06V10/40
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.
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