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公开(公告)号：US10783431B2
公开(公告)日：2020-09-22
申请号：US14938752
申请日：2015-11-11
Applicant: Adobe Inc.
Inventor： Zeke Koch , Gavin Stuart Peter Miller , Jonathan W. Brandt , Nathan A. Carr , Radomir Mech , Walter Wei-Tuh Chang , Scott D. Cohen , Hailin Jin
IPC: G06F17/30 , G06N3/08 , G06F16/583 , G06N20/00 , G06N5/02
Abstract: Image search techniques and systems involving emotions are described. In one or more implementations, a digital medium environment of a content sharing service is described for image search result configuration and control based on a search request that indicates an emotion. The search request is received that includes one or more keywords and specifies an emotion. Images are located that are available for licensing by matching one or more tags associated with the image with the one or more keywords and as corresponding to the emotion. The emotion of the images is identified using one or more models that are trained using machine learning based at least in part on training images having tagged emotions. Output is controlled of a search result having one or more representations of the images that are selectable to license respective images from the content sharing service.
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公开(公告)号：US10783409B2
公开(公告)日：2020-09-22
申请号：US16502608
申请日：2019-07-03
Applicant: Adobe Inc.
Inventor： Hailin Jin , Zhaowen Wang , Gavin Stuart Peter Miller
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：US20200151503A1
公开(公告)日：2020-05-14
申请号：US16184779
申请日：2018-11-08
Applicant: Adobe Inc.
Inventor： Zhaowen Wang , Hailin Jin , Yang Liu
Abstract: In implementations of recognizing text in images, text recognition systems are trained using noisy images that have nuisance factors applied, and corresponding clean images (e.g., without nuisance factors). Clean images serve as supervision at both feature and pixel levels, so that text recognition systems are trained to be feature invariant (e.g., by requiring features extracted from a noisy image to match features extracted from a clean image), and feature complete (e.g., by requiring that features extracted from a noisy image be sufficient to generate a clean image). Accordingly, text recognition systems generalize to text not included in training images, and are robust to nuisance factors. Furthermore, since clean images are provided as supervision at feature and pixel levels, training requires fewer training images than text recognition systems that are not trained with a supervisory clean image, thus saving time and resources.
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公开(公告)号：US10592787B2
公开(公告)日：2020-03-17
申请号：US15807028
申请日：2017-11-08
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 and adversarial training to 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 adversarial trains a font recognition neural network by minimizing font classification loss while at the same time maximizing glyph classification loss. By employing an adversarially trained font classification neural network, the font recognition system can improve overall font recognition by removing the negative side effects from diverse glyph content.
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公开(公告)号：US10592590B2
公开(公告)日：2020-03-17
申请号：US15229108
申请日：2016-08-04
Applicant: ADOBE INC.
Inventor： I-Ming Pao , Alan Lee Erickson , Yuyan Song , Seth Shaw , Hailin Jin , Zhaowen Wang
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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Patent Agency Ranking
公开(公告)号：US20200034671A1
公开(公告)日：2020-01-30
申请号：US16590121
申请日：2019-10-01
Applicant: Adobe Inc.
Inventor： Zhaowen Wang , Luoqi Liu , Hailin Jin
IPC: G06K9/68 , G06K9/46 , G06N3/04 , G06K9/62 , G06K9/00 , G06K9/66 , G06T3/40 , G06K9/52 , 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：US10515296B2
公开(公告)日：2019-12-24
申请号：US15812548
申请日：2017-11-14
Applicant: Adobe Inc.
Inventor： Yang Liu , Zhaowen Wang , I-Ming Pao , Hailin Jin
Abstract: The present disclosure relates to a font recognition system that employs a multi-task learning framework and training to 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 trains a hybrid font recognition neural network that includes two or more font recognition neural networks and a weight prediction neural network. The hybrid font recognition neural network determines and generates classification weights based on which font recognition neural network within the hybrid font recognition neural network is best suited to classify the font in an input text image. By employing a hybrid trained font classification neural network, the font recognition system can improve overall font recognition as well as remove the negative side effects from diverse glyph content.
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公开(公告)号：US20190325277A1
公开(公告)日：2019-10-24
申请号：US16502608
申请日：2019-07-03
Applicant: Adobe Inc.
Inventor： Hailin Jin , Zhaowen Wang , Gavin Stuart Peter Miller
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：US10368047B2
公开(公告)日：2019-07-30
申请号：US15433333
申请日：2017-02-15
Applicant: ADOBE INC.
Inventor： Zhili Chen , Duygu Ceylan Aksit , Jingwei Huang , Hailin Jin
IPC: H04N13/00 , H04N13/117 , H04N5/232 , G06F3/01 , H04N13/144 , H04N13/207 , H04N13/373 , H04N13/376 , H04N13/378 , H04N13/38 , H04N13/366 , G06T15/20 , H04N13/344
Abstract: A stereoscopic six-degree of freedom viewing experience with a monoscopic 360-degree video is provided. A monoscopic 360-degree video of a subject scene can be processed by analyzing each frame to recover a three-dimensional geometric representation, and recover a camera motion path. Utilizing the recovered three-dimensional geometric representation and camera motion path, a dense three-dimensional geometric representation of the subject scene is generated. The processed video can be provided for stereoscopic display via a device. As motion of the device is detected, novel viewpoints can be stereoscopically synthesized for presentation in real time, so as to provide an immersive virtual reality experience based on the original monoscopic 360-degree video and the detected motion of the device.
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公开(公告)号：US20190147304A1
公开(公告)日：2019-05-16
申请号：US15812548
申请日：2017-11-14
Applicant: Adobe Inc.
Inventor： Yang Liu , Zhaowen Wang , I-Ming Pao , Hailin Jin
CPC classification number: G06K9/6828 , G06K9/6227 , G06K9/6257 , G06K9/6262 , G06K9/6277 , G06K9/628 , G06N3/0454 , G06N3/08 , G06N3/084 , G06N5/046
Abstract: The present disclosure relates to a font recognition system that employs a multi-task learning framework and training to 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 trains a hybrid font recognition neural network that includes two or more font recognition neural networks and a weight prediction neural network. The hybrid font recognition neural network determines and generates classification weights based on which font recognition neural network within the hybrid font recognition neural network is best suited to classify the font in an input text image. By employing a hybrid trained font classification neural network, the font recognition system can improve overall font recognition as well as remove the negative side effects from diverse glyph content.
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