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21.发明授权公开(公告)号：US10769764B2
公开(公告)日：2020-09-08
申请号：US16271058
申请日：2019-02-08
Applicant: Adobe Inc.
Inventor： Chen Fang , Zhe Lin , Zhaowen Wang , Yulun Zhang , Yilin Wang , Jimei Yang
Abstract: A style of a digital image is transferred to another digital image of arbitrary resolution. A high-resolution (HR) content image is segmented into several low-resolution (LR) patches. The resolution of a style image is matched to have the same resolution as the LR content image patches. Style transfer is then performed on a patch-by-patch basis using, for example, a pair of feature transforms—whitening and coloring. The patch-by-patch style transfer process is then repeated at several increasing resolutions, or scale levels, of both the content and style images. The results of the style transfer at each scale level are incorporated into successive scale levels up to and including the original HR scale. As a result, style transfer can be performed with images having arbitrary resolutions to produce visually pleasing results with good spatial consistency.
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公开(公告)号：US10664719B2
公开(公告)日：2020-05-26
申请号：US15043174
申请日：2016-02-12
Applicant: ADOBE INC.
Inventor： Zhe Lin , Xiaohui Shen , Jonathan Brandt , Jianming Zhang , Chen Fang
IPC: G06K9/62 , G06K9/46 , G06F16/583 , G06N20/10 , G06F16/51 , G06F16/28 , G06F16/2457 , G06N3/04 , G06N3/08
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：US10334202B1
公开(公告)日：2019-06-25
申请号：US15907497
申请日：2018-02-28
Applicant: Adobe Inc.
Inventor： Yipin Zhou , Zhaowen Wang , Chen Fang , Trung Huu Bui
Abstract: Techniques are disclosed for generating audio based on visual information. In some examples, an audio generation system is trained using supervised learning using a training set generated from videos. The trained audio generation system is able to infer audio for provided silent video based on the visual contents of the silent video, and generate raw waveform samples that represent the inferred audio.
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公开(公告)号：US11238362B2
公开(公告)日：2022-02-01
申请号：US14996959
申请日：2016-01-15
Applicant: Adobe Inc.
Inventor： Hailin Jin , Zhou Ren , Zhe Lin , Chen Fang
Abstract: Modeling semantic concepts in an embedding space as distributions is described. In the embedding space, both images and text labels are represented. The text labels describe semantic concepts that are exhibited in image content. In the embedding space, the semantic concepts described by the text labels are modeled as distributions. By using distributions, each semantic concept is modeled as a continuous cluster which can overlap other clusters that model other semantic concepts. For example, a distribution for the semantic concept “apple” can overlap distributions for the semantic concepts “fruit” and “tree” since can refer to both a fruit and a tree. In contrast to using distributions, conventionally configured visual-semantic embedding spaces represent a semantic concept as a single point. Thus, unlike these conventionally configured embedding spaces, the embedding spaces described herein are generated to model semantic concepts as distributions, such as Gaussian distributions, Gaussian mixtures, and so on.
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25.发明申请公开(公告)号：US20200349688A1
公开(公告)日：2020-11-05
申请号：US16930736
申请日：2020-07-16
Applicant: Adobe Inc.
Inventor： Chen Fang , Zhe Lin , Zhaowen Wang , Yulun Zhang , Yilin Wang , Jimei Yang
Abstract: A style of a digital image is transferred to another digital image of arbitrary resolution. A high-resolution (HR) content image is segmented into several low-resolution (LR) patches. The resolution of a style image is matched to have the same resolution as the LR content image patches. Style transfer is then performed on a patch-by-patch basis using, for example, a pair of feature transforms—whitening and coloring. The patch-by-patch style transfer process is then repeated at several increasing resolutions, or scale levels, of both the content and style images. The results of the style transfer at each scale level are incorporated into successive scale levels up to and including the original HR scale. As a result, style transfer can be performed with images having arbitrary resolutions to produce visually pleasing results with good spatial consistency.
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Patent Agency Ranking
公开(公告)号：US10762135B2
公开(公告)日：2020-09-01
申请号：US15357864
申请日：2016-11-21
Applicant: Adobe Inc.
Inventor： Matthew Douglas Hoffman , Longqi Yang , Hailin Jin , Chen Fang
IPC: G06F16/9032 , G06F16/2457 , G06F40/166
Abstract: A digital medium environment includes an asset processing application that performs editing of assets. A projection function is trained using pairs of actions pertaining to software edits, and assets resulting from the actions to learn a joint embedding between the actions and the assets. The projection function is used in the asset processing application to recommend software actions to create an asset, and also to recommend assets to demonstrate the effects of software actions. Recommendations are based on ranking distance measures that measure distances between actions representations and asset representations in a vector space.
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27.发明申请公开(公告)号：US20200258204A1
公开(公告)日：2020-08-13
申请号：US16271058
申请日：2019-02-08
Applicant: Adobe Inc.
Inventor： Chen Fang , Zhe Lin , Zhaowen Wang , Yulun Zhang , Yilin Wang , Jimei Yang
Abstract: A style of a digital image is transferred to another digital image of arbitrary resolution. A high-resolution (HR) content image is segmented into several low-resolution (LR) patches. The resolution of a style image is matched to have the same resolution as the LR content image patches. Style transfer is then performed on a patch-by-patch basis using, for example, a pair of feature transforms—whitening and coloring. The patch-by-patch style transfer process is then repeated at several increasing resolutions, or scale levels, of both the content and style images. The results of the style transfer at each scale level are incorporated into successive scale levels up to and including the original HR scale. As a result, style transfer can be performed with images having arbitrary resolutions to produce visually pleasing results with good spatial consistency.
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28.发明申请公开(公告)号：US20200151938A1
公开(公告)日：2020-05-14
申请号：US16184289
申请日：2018-11-08
Applicant: Adobe Inc.
Inventor： Elya Shechtman , Yijun Li , Chen Fang , Aaron Hertzmann
Abstract: This disclosure relates to methods, non-transitory computer readable media, and systems that integrate (or embed) a non-photorealistic rendering (“NPR”) generator with a style-transfer-neural network to generate stylized images that both correspond to a source image and resemble a stroke style. By integrating an NPR generator with a style-transfer-neural network, the disclosed methods, non-transitory computer readable media, and systems can accurately capture a stroke style resembling one or both of stylized edges or stylized shadings. When training such a style-transfer-neural network, the integrated NPR generator can enable the disclosed methods, non-transitory computer readable media, and systems to use real-stroke drawings (instead of conventional paired-ground-truth drawings) for training the network to accurately portray a stroke style. In some implementations, the disclosed methods, non-transitory computer readable media, and systems can either train or apply a style-transfer-neural network that captures a variety of stroke styles, such as different edge-stroke styles or shading-stroke styles.
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29.发明申请公开(公告)号：US20190272451A1
公开(公告)日：2019-09-05
申请号：US16417115
申请日：2019-05-20
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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公开(公告)号：US20190251612A1
公开(公告)日：2019-08-15
申请号：US15897856
申请日：2018-02-15
Applicant: Adobe Inc. , The Regents of the University of California
Inventor： Chen Fang , Zhaowen Wang , Wangcheng Kang , Julian McAuley
CPC classification number: G06Q30/0621 , G06F16/532 , G06N3/08
Abstract: The present disclosure relates to a personalized fashion generation system that synthesizes user-customized images using deep learning techniques based on visually-aware user preferences. In particular, the personalized fashion generation system employs an image generative adversarial neural network and a personalized preference network to synthesize new fashion items that are individually customized for a user. Additionally, the personalized fashion generation system can modify existing fashion items to tailor the fashion items to a user's tastes and preferences.
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