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公开(公告)号：US20180039605A1
公开(公告)日：2018-02-08
申请号：US15229108
申请日：2016-08-04
Applicant: ADOBE SYSTEMS INCORPORATED
Inventor： I-Ming Pao , Alan Lee Erickson , Yuyan Song , Seth Shaw , Hailin Jin , Zhaowen Wang
IPC: G06F17/21 , G06Q20/40 , G06Q20/12 , G06F3/0482
Abstract: Embodiments of the present invention are directed at providing a font similarity preview for non-resident fonts. In one embodiment, a font is selected on a computing device. In response to the selection of the font, a pre-computed font list is checked to determine what fonts are similar to the selected font. In response to a determination that similar fonts are not local to the computing device, a non-resident font list is sent to a font vendor. The font vendor sends back previews of the non-resident fonts based on entitlement information of a user. Further, a full non-resident font can be synced to the computing device. Other embodiments may be described and/or claimed.
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公开(公告)号：US09824304B2
公开(公告)日：2017-11-21
申请号：US14876660
申请日：2015-10-06
Applicant: Adobe Systems Incorporated
Inventor： Zhaowen Wang , Hailin Jin
IPC: G06K9/00 , G06K9/18 , G06K9/32 , G09G1/10 , G09G1/14 , G06T11/20 , G06K9/68 , G06K9/66 , G06K9/62 , G06T7/60
CPC classification number: G06K9/6828 , G06K9/6267 , G06K9/627 , G06K9/66 , G06K2209/01 , G06K2209/27 , G06N3/0454 , G06T7/60 , G06T2207/20081 , G06T2207/20084
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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公开(公告)号：US09799102B2
公开(公告)日：2017-10-24
申请号：US14957539
申请日：2015-12-02
Applicant: Adobe Systems Incorporated
Inventor： Nathan A. Carr , Zhaowen Wang , Duygu Ceylan , I-Chao Shen
CPC classification number: G06T5/002 , G06K9/44 , G06K9/6256 , G06T7/13 , G06T2207/20081
Abstract: Smoothing images using machine learning is described. In one or more embodiments, a machine learning system is trained using multiple training items. Each training item includes a boundary shape representation and a positional indicator. To generate the training item, a smooth image is downscaled to produce a corresponding blocky image that includes multiple blocks. For a given block, the boundary shape representation encodes a blocky boundary in a neighborhood around the given block. The positional indicator reflects a distance between the given block and a smooth boundary of the smooth image. In one or more embodiments to smooth a blocky image, a boundary shape representation around a selected block is determined. The representation is encoded as a feature vector and applied to the machine learning system to obtain a positional indicator. The positional indicator is used to compute a location of a smooth boundary of a smooth image.
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公开(公告)号：US20170200066A1
公开(公告)日：2017-07-13
申请号：US14995042
申请日：2016-01-13
Applicant: Adobe Systems Incorporated
Inventor： Zhaowen Wang , Quanzeng You , Hailin Jin , Chen Fang
CPC classification number: G06K9/6269 , G06F17/30253 , G06F17/3028 , G06K9/00664 , G06K9/4604 , G06K9/4628 , G06K9/6202 , G06K9/6274 , G06N3/08
Abstract: Techniques for image captioning with word vector representations are described. In implementations, instead of outputting results of caption analysis directly, the framework is adapted to output points in a semantic word vector space. These word vector representations reflect distance values in the context of the semantic word vector space. In this approach, words are mapped into a vector space and the results of caption analysis are expressed as points in the vector space that capture semantics between words. In the vector space, similar concepts with have small distance values. The word vectors are not tied to particular words or a single dictionary. A post-processing step is employed to map the points to words and convert the word vector representations to captions. Accordingly, conversion is delayed to a later stage in the process.
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公开(公告)号：US20170098138A1
公开(公告)日：2017-04-06
申请号：US14876667
申请日：2015-10-06
Applicant: Adobe Systems Incorporated
Inventor： Zhaowen Wang , Luoqi Liu , Hailin Jin
CPC classification number: G06K9/6257 , G06K9/4628 , G06K9/6272 , G06K9/6277 , G06K9/66 , G06K9/6828 , G06K2209/01 , G06N3/0454
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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Patent Agency Ranking
公开(公告)号：US20180373979A1
公开(公告)日：2018-12-27
申请号：US15630604
申请日：2017-06-22
Applicant: Adobe Systems Incorporated
Inventor： Zhaowen Wang , Shuai Tang , Hailin Jin , Chen Fang
Abstract: The present disclosure includes methods and systems for generating captions for digital images. In particular, the disclosed systems and methods can train an image encoder neural network and a sentence decoder neural network to generate a caption from an input digital image. For instance, in one or more embodiments, the disclosed systems and methods train an image encoder neural network (e.g., a character-level convolutional neural network) utilizing a semantic similarity constraint, training images, and training captions. Moreover, the disclosed systems and methods can train a sentence decoder neural network (e.g., a character-level recurrent neural network) utilizing training sentences and an adversarial classifier.
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公开(公告)号：US10074042B2
公开(公告)日：2018-09-11
申请号：US14876609
申请日：2015-10-06
Applicant: Adobe Systems Incorporated
Inventor： Zhaowen Wang , Luoqi Liu , Hailin Jin
CPC classification number: G06K9/6828 , G06K9/00442 , G06K9/46 , G06K9/4628 , G06K9/52 , G06K9/6272 , G06K9/66 , G06K2009/4666 , G06K2209/01 , G06N3/0454 , G06T3/40 , G06T7/60
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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公开(公告)号：US10007868B2
公开(公告)日：2018-06-26
申请号：US15269492
申请日：2016-09-19
Applicant: Adobe Systems Incorporated
Inventor： Hailin Jin , Zhaowen Wang , Gavin Stuart Peter Miller
CPC classification number: G06K9/6828 , G06F17/214 , G06N3/0454 , G06N3/08 , G06N5/025 , G06N7/005
Abstract: Font replacement based on visual similarity is described. In one or more embodiments, a font descriptor includes multiple font features derived from a visual appearance of a font by a font visual similarity model. The font visual similarity model can be trained using a machine learning system that recognizes similarity between visual appearances of two different fonts. A source computing device embeds a font descriptor in a document, which is transmitted to a destination computing device. The destination compares the embedded font descriptor to font descriptors corresponding to local fonts. Based on distances between the embedded and the local font descriptors, at least one matching font descriptor is determined. The local font corresponding to the matching font descriptor is deemed similar to the original font. The destination computing device controls presentations of the document using the similar local font. Computation of font descriptors can be outsourced to a remote location.
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公开(公告)号：US20180158199A1
公开(公告)日：2018-06-07
申请号：US15676903
申请日：2017-08-14
Applicant: Adobe Systems Incorporated
Inventor： Zhaowen Wang , Hailin Jin
CPC classification number: G06T7/33 , G06K9/00261 , G06K9/4604 , G06K9/4671 , G06K9/6202 , G06K9/6211 , G06K2009/3291 , G06T3/0068 , G06T3/0093 , G06T5/002 , G06T5/50 , G06T2207/10016 , G06T2207/20016 , G06T2207/20021 , G06T2207/20182 , G06T2207/20221
Abstract: The present disclosure is directed towards systems and methods for generating a new aligned image from a plurality of burst image. The systems and methods subdivide a reference image into a plurality of local regions and a subsequent image into a plurality of corresponding local regions. Additionally, the systems and methods detect a plurality of feature points in each of the reference image and the subsequent image and determine matching feature point pairs between the reference image and the subsequent image. Based on the matching feature point pairs, the systems and methods determine at least one homography of the reference image to the subsequent image. Based on the homography, the systems and methods generate a new aligned image that is that is pixel-wise aligned to the reference image. Furthermore, the systems and methods refines boundaries between local regions of the new aligned image.
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公开(公告)号：US09965717B2
公开(公告)日：2018-05-08
申请号：US14940916
申请日：2015-11-13
Applicant: ADOBE SYSTEMS INCORPORATED
Inventor： Zhaowen Wang , Xianming Liu , Hailin Jin , Chen Fang
CPC classification number: G06N3/0454 , G06K9/4628 , G06K9/6257 , G06K9/627 , G06K9/628 , G06K9/6284 , G06K9/629 , G06N3/04 , G06N3/08 , G06Q50/01
Abstract: Embodiments of the present invention relate to learning image representation by distilling from multi-task networks. In implementation, more than one single-task network is trained with heterogeneous labels. In some embodiments, each of the single-task networks is transformed into a Siamese structure with three branches of sub-networks so that a common triplet ranking loss can be applied to each branch. A distilling network is trained that approximates the single-task networks on a common ranking task. In some embodiments, the distilling network is a Siamese network whose ranking function is optimized to approximate an ensemble ranking of each of the single-task networks. The distilling network can be utilized to predict tags to associate with a test image or identify similar images to the test image.
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