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公开(公告)号：US20210192594A1
公开(公告)日：2021-06-24
申请号：US17192713
申请日：2021-03-04
Applicant: Adobe Inc. , The Regents of the University of California
Inventor： Chen Fang , Zhaowen Wang , Wangcheng Kang , Julian McAuley
IPC: G06Q30/06 , G06N3/08 , G06F16/532
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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公开(公告)号：US10984295B2
公开(公告)日：2021-04-20
申请号：US16590121
申请日：2019-10-01
Applicant: Adobe Inc.
Inventor： Zhaowen Wang , Luoqi Liu , Hailin Jin
IPC: G06K9/68 , G06K9/00 , G06K9/66 , G06K9/46 , G06T3/40 , G06K9/52 , G06T7/60 , G06N3/04 , G06K9/62
Abstract: Font recognition and similarity determination techniques and systems are described. In a first example, localization techniques are described to train a model using machine learning (e.g., a convolutional neural network) using training images. The model is then used to localize text in a subsequently received image, and may do so automatically and without user intervention, e.g., without specifying any of the edges of a bounding box. In a second example, a deep neural network is directly learned as an embedding function of a model that is usable to determine font similarity. In a third example, techniques are described that leverage attributes described in metadata associated with fonts as part of font recognition and similarity determinations.
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33.发明申请公开(公告)号：US20200285916A1
公开(公告)日：2020-09-10
申请号：US16294417
申请日：2019-03-06
Applicant: Adobe Inc.
Inventor： Zhaowen Wang , Tianlang Chen , Ning Xu , Hailin Jin
IPC: G06K9/68 , G06K9/62 , G06K9/46 , G06F17/21 , G06N3/08 , G06F16/903 , G06F16/906
Abstract: The present disclosure relates to a tag-based font recognition system that utilizes a multi-learning framework to develop and improve tag-based font recognition using deep learning neural networks. In particular, the tag-based font recognition system jointly trains a font tag recognition neural network with an implicit font classification attention model to generate font tag probability vectors that are enhanced by implicit font classification information. Indeed, the font recognition system weights the hidden layers of the font tag recognition neural network with implicit font information to improve the accuracy and predictability of the font tag recognition neural network, which results in improved retrieval of fonts in response to a font tag query. Accordingly, using the enhanced tag probability vectors, the tag-based font recognition system can accurately identify and recommend one or more fonts in response to a font tag query.
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公开(公告)号：US20200226724A1
公开(公告)日：2020-07-16
申请号：US16246051
申请日：2019-01-11
Applicant: Adobe Inc.
Inventor： Chen Fang , Zhe Lin , Zhaowen Wang , Yulun Zhang , Yilin Wang , Jimei Yang
Abstract: In implementations of transferring image style to content of a digital image, an image editing system includes an encoder that extracts features from a content image and features from a style image. A whitening and color transform generates coarse features from the content and style features extracted by the encoder for one pass of encoding and decoding. Hence, the processing delay and memory requirements are low. A feature transfer module iteratively transfers style features to the coarse feature map and generates a fine feature map. The image editing system fuses the fine features with the coarse features, and a decoder generates an output image with content of the content image in a style of the style image from the fused features. Accordingly, the image editing system efficiently transfers an image style to image content in real-time, without undesirable artifacts in the output image.
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公开(公告)号：US10552944B2
公开(公告)日：2020-02-04
申请号：US15784039
申请日：2017-10-13
Applicant: Adobe Inc.
Inventor： Zhaowen Wang
Abstract: Systems and techniques for converting a low resolution image to a high resolution image include receiving a low resolution image having one or more noise artifacts at a neural network. A noise reduction level is received at the neural network. The neural network determines a network parameter based on the noise reduction level. The neural network converts the low resolution image to a high resolution image and removes one or more of the noise artifacts from the low resolution image during the converting by the using the network parameter. The neural network outputs the high resolution image.
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Patent Agency Ranking
公开(公告)号：US10515295B2
公开(公告)日：2019-12-24
申请号：US15796213
申请日：2017-10-27
Applicant: Adobe Inc.
Inventor： Yang Liu , Zhaowen Wang , Hailin Jin
Abstract: The present disclosure relates to a font recognition system that employs a multi-task learning framework to jointly improve font classification and remove negative side effects caused by intra-class variances of glyph content. For example, in one or more embodiments, the font recognition system can jointly train a font recognition neural network using a font classification loss model and triplet loss model to generate a deep learning neural network that provides improved font classifications. In addition, the font recognition system can employ the trained font recognition neural network to efficiently recognize fonts within input images as well as provide other suggested fonts.
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公开(公告)号：US20190385346A1
公开(公告)日：2019-12-19
申请号：US16010110
申请日：2018-06-15
Applicant: Adobe Inc.
Inventor： Matthew David Fisher , Samaneh Azadi , Vladimir Kim , Elya Shechtman , Zhaowen Wang
IPC: G06T11/20
Abstract: Techniques are disclosed for the synthesis of a full set of slotted content, based upon only partial observations of the slotted content. With respect to a font, the slots may comprise particular letters or symbols or glyphs in an alphabet. Based upon partial observations of a subset of glyphs from a font, a full set of the glyphs corresponding to the font may be synthesized and may further be ornamented.
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公开(公告)号：US20190333198A1
公开(公告)日：2019-10-31
申请号：US15962735
申请日：2018-04-25
Applicant: Adobe Inc.
Inventor： Yilin Wang , Zhe Lin , Zhaowen Wang , Xin Lu , Xiaohui Shen , Chih-Yao Hsieh
IPC: G06T5/50
Abstract: The present disclosure relates to training and utilizing an image exposure transformation network to generate a long-exposure image from a single short-exposure image (e.g., still image). In various embodiments, the image exposure transformation network is trained using adversarial learning, long-exposure ground truth images, and a multi-term loss function. In some embodiments, the image exposure transformation network includes an optical flow prediction network and/or an appearance guided attention network. Trained embodiments of the image exposure transformation network generate realistic long-exposure images from single short-exposure images without additional information.
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39.发明申请公开(公告)号：US20190251446A1
公开(公告)日：2019-08-15
申请号：US15897822
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
Abstract: The present disclosure relates to a fashion recommendation system that employs a task-guided learning framework to jointly train a visually-aware personalized preference ranking network. In addition, the fashion recommendation system employs implicit feedback and generated user-based triplets to learn variances in the user's fashion preferences for items with which the user has not yet interacted. In particular, the fashion recommendation system uses triplets generated from implicit user data to jointly train a Siamese convolutional neural network and a personalized ranking model, which together produce a user preference predictor that determines personalized fashion recommendations for a user.
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公开(公告)号：US20190130231A1
公开(公告)日：2019-05-02
申请号：US15796213
申请日：2017-10-27
Applicant: Adobe Inc.
Inventor： Yang Liu , Zhaowen Wang , Hailin Jin
Abstract: The present disclosure relates to a font recognition system that employs a multi-task learning framework to jointly improve font classification and remove negative side effects caused by intra-class variances of glyph content. For example, in one or more embodiments, the font recognition system can jointly train a font recognition neural network using a font classification loss model and triplet loss model to generate a deep learning neural network that provides improved font classifications. In addition, the font recognition system can employ the trained font recognition neural network to efficiently recognize fonts within input images as well as provide other suggested fonts.
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