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公开(公告)号：US10192321B2
公开(公告)日：2019-01-29
申请号：US15409321
申请日：2017-01-18
Applicant: ADOBE INC.
Inventor： Chen Fang , Zhaowen Wang , Yijun Li , Jimei Yang
IPC: G06T15/04 , G06T7/40 , G06T11/00 , G06T5/50 , G06F3/0482
Abstract: Systems and techniques that synthesize an image with similar texture to a selected style image. A generator network is trained to synthesize texture images depending on a selection unit input. The training configures the generator network to synthesize texture images that are similar to individual style images of multiple style images based on which is selected by the selection unit input. The generator network can be configured to minimize a covariance matrix-based style loss and/or a diversity loss in synthesizing the texture images. After training the generator network, the generator network is used to synthesize texture images for selected style images. For example, this can involve receiving user input selecting a selected style image, determining the selection unit input based on the selected style image, and synthesizing texture images using the generator network with the selection unit input and noise input.
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公开(公告)号：US11816888B2
公开(公告)日：2023-11-14
申请号：US16853111
申请日：2020-04-20
Applicant: Adobe Inc.
Inventor： Zhe Lin , Xiaohui Shen , Jonathan Brandt , Jianming Zhang , Chen Fang
IPC: G06F16/51 , G06F16/28 , G06F16/2457 , G06F16/583 , G06F18/2113 , G06F18/21 , G06F18/23213 , G06F18/2413 , G06N3/045 , G06N20/10 , G06V20/00 , G06V10/762 , G06V10/764 , G06N3/08
CPC classification number: G06V20/35 , G06F16/24578 , G06F16/285 , G06F16/51 , G06F16/583 , G06F18/217 , G06F18/2113 , G06F18/23213 , G06F18/24147 , G06N3/045 , G06N3/08 , G06V10/763 , G06V10/764 , G06N20/10
Abstract: Embodiments of the present invention provide an automated image tagging system that can predict a set of tags, along with relevance scores, that can be used for keyword-based image retrieval, image tag proposal, and image tag auto-completion based on user input. Initially, during training, a clustering technique is utilized to reduce cluster imbalance in the data that is input into a convolutional neural network (CNN) for training feature data. In embodiments, the clustering technique can also be utilized to compute data point similarity that can be utilized for tag propagation (to tag untagged images). During testing, a diversity based voting framework is utilized to overcome user tagging biases. In some embodiments, bigram re-weighting can down-weight a keyword that is likely to be part of a bigram based on a predicted tag set.
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公开(公告)号：US11783461B2
公开(公告)日：2023-10-10
申请号：US17170209
申请日：2021-02-08
Applicant: Adobe Inc.
Inventor： Jingwan Lu , Patsorn Sangkloy , Chen Fang
CPC classification number: G06T5/20 , G06N3/045 , G06N3/08 , G06T11/00 , G06T11/001 , G06T2207/20081 , G06T2207/20084
Abstract: Methods and systems are provided for transforming sketches into stylized electronic paintings. A neural network system is trained where the training includes training a first neural network that converts input sketches into output images and training a second neural network that converts images into output paintings. Similarity for the first neural network is evaluated between the output image and a reference image and similarity for the second neural network is evaluated between the output painting, the output image, and a reference painting. The neural network system is modified based on the evaluated similarity. The trained neural network is used to generate an output painting from an input sketch where the output painting maintains features from the input sketch utilizing an extrapolated intermediate image and reflects a designated style from the reference painting.
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34.发明授权公开(公告)号：US11630952B2
公开(公告)日：2023-04-18
申请号：US16518894
申请日：2019-07-22
Applicant: Adobe Inc.
Inventor： Sean MacAvaney , Franck Dernoncourt , Walter Chang , Seokhwan Kim , Doo Soon Kim , Chen Fang
IPC: G06F17/15 , G06F17/16 , G06N3/045 , G06V10/80 , G06V10/82 , G06F40/279 , G06F18/2431 , G06V10/764 , G06V10/70
Abstract: This disclosure relates to methods, non-transitory computer readable media, and systems that can classify term sequences within a source text based on textual features analyzed by both an implicit-class-recognition model and an explicit-class-recognition model. For example, by applying machine-learning models for both implicit and explicit class recognition, the disclosed systems can determine a class corresponding to a particular term sequence within a source text and identify the particular term sequence reflecting the class. The dual-model architecture can equip the disclosed systems to apply (i) the implicit-class-recognition model to recognize implicit references to a class in source texts and (ii) the explicit-class-recognition model to recognize explicit references to the same class in source texts.
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公开(公告)号：US11381651B2
公开(公告)日：2022-07-05
申请号：US16424949
申请日：2019-05-29
Applicant: ADOBE INC.
Inventor： Handong Zhao , Zhiqiang Tao , Zhaowen Wang , Sheng Li , Chen Fang
IPC: H04L29/08 , G06N3/08 , G06N3/04 , H04L67/50 , H04L67/306
Abstract: Methods and systems are provided for generating interpretable user modeling system. The interpretable user modeling system can use an intent neural network to implement one or more tasks. The intent neural network can bridge a semantic gap between log data and human language by leveraging tutorial data to understand user logs in a semantically meaningful way. A memory unit of the intent neural network can capture information from the tutorial data. Such a memory unit can be queried to identify human readable sentences related to actions received by the intent neural network. The human readable sentences can be used to interpret the user log data in a semantically meaningful way.
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Patent Agency Ranking
公开(公告)号：US20210158494A1
公开(公告)日：2021-05-27
申请号：US17170209
申请日：2021-02-08
Applicant: Adobe Inc.
Inventor： Jingwan Lu , Patsorn Sangkloy , Chen Fang
Abstract: Methods and systems are provided for transforming sketches into stylized electronic paintings. A neural network system is trained where the training includes training a first neural network that converts input sketches into output images and training a second neural network that converts images into output paintings. Similarity for the first neural network is evaluated between the output image and a reference image and similarity for the second neural network is evaluated between the output painting, the output image, and a reference painting. The neural network system is modified based on the evaluated similarity. The trained neural network is used to generate an output painting from an input sketch where the output painting maintains features from the input sketch utilizing an extrapolated intermediate image and reflects a designated style from the reference painting.
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公开(公告)号：US20200382612A1
公开(公告)日：2020-12-03
申请号：US16424949
申请日：2019-05-29
Applicant: ADOBE INC.
Inventor： Handong Zhao , Zhiqiang Tao , Zhaowen Wang , Sheng Li , Chen Fang
Abstract: Methods and systems are provided for generating interpretable user modeling system. The interpretable user modeling system can use an intent neural network to implement one or more tasks. The intent neural network can bridge a semantic gap between log data and human language by leveraging tutorial data to understand user logs in a semantically meaningful way. A memory unit of the intent neural network can capture information from the tutorial data. Such a memory unit can be queried to identify human readable sentences related to actions received by the intent neural network. The human readable sentences can be used to interpret the user log data in a semantically meaningful way.
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公开(公告)号：US10482639B2
公开(公告)日：2019-11-19
申请号：US15438147
申请日：2017-02-21
Applicant: Adobe Inc.
Inventor： Yijun Li , Chen Fang , Jimei Yang , Zhaowen Wang , Xin Lu
Abstract: In some embodiments, techniques for synthesizing an image style based on a plurality of neural networks are described. A computer system selects a style image based on user input that identifies the style image. The computer system generates an image based on a generator neural network and a loss neural network. The generator neural network outputs the synthesized image based on a noise vector and the style image and is trained based on style features generated from the loss neural network. The loss neural network outputs the style features based on a training image. The training image and the style image have a same resolution. The style features are generated at different resolutions of the training image. The computer system provides the synthesized image to a user device in response to the user input.
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公开(公告)号：US10445921B1
公开(公告)日：2019-10-15
申请号：US16007898
申请日：2018-06-13
Applicant: Adobe Inc.
Inventor： Yijun Li , Chen Fang , Jimei Yang , Zhaowen Wang , Xin Lu
Abstract: Transferring motion between consecutive frames to a digital image is leveraged in a digital medium environment. A digital image and at least a portion of the digital video are exposed to a motion transfer model. The portion of the digital video includes at least a first digital video frame and a second digital video frame that is consecutive to the first digital video frame. Flow data between the first digital video frame and the second digital image frame is extracted, and the flow data is then processed to generate motion features representing motion between the first digital video frame and the second digital video frame. The digital image is processed to generate image features of the digital image. Motion of the digital video is then transferred to the digital image by combining the motion features with the image features to generate a next digital image frame for the digital image.
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40.发明授权公开(公告)号：US10346727B2
公开(公告)日：2019-07-09
申请号：US15429769
申请日：2017-02-10
Applicant: Adobe Inc.
Inventor： Zhe Lin , Mai Long , Jonathan Brandt , Hailin Jin , Chen Fang
Abstract: The present disclosure includes methods and systems for searching for digital visual media based on semantic and spatial information. In particular, one or more embodiments of the disclosed systems and methods identify digital visual media displaying targeted visual content in a targeted region based on a query term and a query area provide via a digital canvas. Specifically, the disclosed systems and methods can receive user input of a query term and a query area and provide the query term and query area to a query neural network to generate a query feature set. Moreover, the disclosed systems and methods can compare the query feature set to digital visual media feature sets. Further, based on the comparison, the disclosed systems and methods can identify digital visual media portraying targeted visual content corresponding to the query term within a targeted region corresponding to the query area.
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