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公开(公告)号：US20230177824A1
公开(公告)日：2023-06-08
申请号：US18161666
申请日：2023-01-30
Applicant: Adobe Inc.
Inventor： Brian Price , Scott Cohen , Mai Long , Jun Hao Liew
IPC: G06V10/82 , G06N3/084 , G06T7/11 , G06V10/26 , G06V10/44 , G06F18/40 , G06N3/045 , G06N5/01 , G06V10/94 , G06V10/20
CPC classification number: G06V10/82 , G06N3/084 , G06T7/11 , G06V10/26 , G06V10/454 , G06F18/40 , G06N3/045 , G06N5/01 , G06V10/945 , G06V10/255 , G06N3/044
Abstract: Systems and methods are disclosed for selecting target objects within digital images utilizing a multi-modal object selection neural network trained to accommodate multiple input modalities. 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 corresponding to various input modalities. Moreover, one or more embodiments of the disclosed systems and methods utilize a trained neural network and iterative user inputs corresponding to different input modalities to select target objects in digital images. Specifically, the disclosed systems and methods can transform user inputs 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.
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公开(公告)号：US20230135978A1
公开(公告)日：2023-05-04
申请号：US17513559
申请日：2021-10-28
Applicant: Adobe Inc.
Inventor： Brian Price , Yutong Dai , He Zhang
Abstract: Methods, systems, and non-transitory computer readable storage media are disclosed for utilizing a transformer-based encoder-decoder neural network architecture for generating alpha mattes for digital images. Specifically, the disclosed system utilizes a transformer encoder to generate patch-based encodings from a digital image and a trimap segmentation by generating patch encodings for image patches and comparing the patch encodings to areas of the digital image. Additionally, the disclosed system generates modified patch-based encodings utilizing a plurality of neural network layers. The disclosed system also generates an alpha matte for the digital image from the patch-based encodings utilizing a decoder that includes a plurality of upsampling layers connected to a plurality of neural network layers via skip connections. In additional embodiments, the disclosed system generates the alpha matte based on additional encodings generated by a plurality of convolutional neural network layers connected to a subset of the upsampling layers via skip connections.
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公开(公告)号：US11587234B2
公开(公告)日：2023-02-21
申请号：US17151111
申请日：2021-01-15
Applicant: Adobe Inc.
Inventor： Yinan Zhao , Brian Price , Scott Cohen
Abstract: The present disclosure relates to a class-agnostic object segmentation system that automatically detects, segments, and selects objects within digital images irrespective of object semantic classifications. For example, the object segmentation system utilizes a class-agnostic object segmentation neural network to segment each pixel in a digital image into an object mask. Further, in response to detecting a selection request of a target object, the object segmentation system utilizes a corresponding object mask to automatically select the target object within the digital image. In some implementations, the object segmentation system utilizes a class-agnostic object segmentation neural network to detect and automatically select a partial object in the digital image in response to a target object selection request.
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公开(公告)号：US11379987B2
公开(公告)日：2022-07-05
申请号：US17020023
申请日：2020-09-14
Applicant: ADOBE INC.
Inventor： Ning Xu , Brian Price , Scott Cohen
IPC: G06T7/11 , G06T7/194 , G06T7/73 , G11B27/031
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：US11044450B2
公开(公告)日：2021-06-22
申请号：US16435371
申请日：2019-06-07
Applicant: Adobe Inc. , York University
Inventor： Mahmoud Afifi , Michael Brown , Brian Price , Scott Cohen
Abstract: Techniques are described for white balancing an input image by determining a color transformation for the input image based on color transformations that have been computed for training images whose color characteristics are similar to those of the input image. Techniques are also described for generating a training dataset comprising color information for a plurality of training images and color transformation information for the plurality of training images. The color information in the training dataset is searched to identify a subset of training images that are most similar in color to the input image. The color transformation for the input image is then computed by combining color transformation information for the identified training images. The contribution of the color transformation information for any given training image to the combination can be weighted based on the degree of color similarity between the input image and the training image.
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Patent Agency Ranking
公开(公告)号：US10922860B2
公开(公告)日：2021-02-16
申请号：US16410854
申请日：2019-05-13
Applicant: Adobe Inc.
Inventor： Brian Price , Ning Xu , Naoto Inoue , Jimei Yang , Daicho Ito
Abstract: Computing systems and computer-implemented methods can be used for automatically generating a digital line drawing of the contents of a photograph. In various examples, these techniques include use of a neural network, referred to as a generator network, that is trained on a dataset of photographs and human-generated line drawings of the photographs. The training data set teaches the neural network to trace the edges and features of objects in the photographs, as well as which edges or features can be ignored. The output of the generator network is a two-tone digital image, where the background of the image is one tone, and the contents in the input photographs are represented by lines drawn in the second tone. In some examples, a second neural network, referred to as a restorer network, can further process the output of the generator network, and remove visual artifacts and clean up the lines.
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公开(公告)号：US10754851B2
公开(公告)日：2020-08-25
申请号：US15852506
申请日：2017-12-22
Applicant: Adobe Inc.
Inventor： Scott Cohen , Kushal Kafle , Brian Price
IPC: G06F16/30 , G06F16/242 , G06T11/20 , G06K9/18 , G06N3/02 , G06F16/26 , G06F16/248 , G06F16/2457 , G06N3/04 , G06N3/08 , G06N5/04
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.
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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.
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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.
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公开(公告)号：US12165292B2
公开(公告)日：2024-12-10
申请号：US18317547
申请日：2023-05-15
Applicant: Adobe Inc.
Inventor： He Zhang , Seyed Morteza Safdarnejad , Yilin Wang , Zijun Wei , Jianming Zhang , Salil Tambe , Brian Price
IPC: G06T5/75 , G06N3/08 , G06T3/4046 , G06T7/194
Abstract: Methods, systems, and non-transitory computer readable storage media are disclosed for utilizing a plurality of neural networks in a multi-branch pipeline to generate image masks for digital images. Specifically, the disclosed system can classify a digital image as a portrait or a non-portrait image. Based on classifying a portrait image, the disclosed system can utilize separate neural networks to generate a first mask portion for a portion of the digital image including a defined boundary region and a second mask portion for a portion of the digital image including a blended boundary region. The disclosed system can generate the mask portion for the blended boundary region by utilizing a trimap generation neural network to automatically generate a trimap segmentation including the blended boundary region. The disclosed system can then merge the first mask portion and the second mask portion to generate an image mask for the digital image.
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