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公开(公告)号：US11244167B2
公开(公告)日：2022-02-08
申请号：US16784005
申请日：2020-02-06
Applicant: Adobe Inc.
Inventor： Wentian Zhao , Seokhwan Kim , Ning Xu , Hailin Jin
Abstract: The present disclosure relates to systems, methods, and non-transitory computer-readable media for generating a response to a question received from a user during display or playback of a video segment by utilizing a query-response-neural network. The disclosed systems can extract a query vector from a question corresponding to the video segment using the query-response-neural network. The disclosed systems further generate context vectors representing both visual cues and transcript cues corresponding to the video segment using context encoders or other layers from the query-response-neural network. By utilizing additional layers from the query-response-neural network, the disclosed systems generate (i) a query-context vector based on the query vector and the context vectors, and (ii) candidate-response vectors representing candidate responses to the question from a domain-knowledge base or other source. To respond to a user's question, the disclosed systems further select a response from the candidate responses based on a comparison of the query-context vector and the candidate-response vectors.
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公开(公告)号：US20210248432A1
公开(公告)日：2021-08-12
申请号：US16788781
申请日：2020-02-12
Applicant: ADOBE INC.
Inventor： Zhaowen Wang , Zhifei Zhang , Xuan Li , Matthew Fisher , Hailin Jin
IPC: G06K15/02
Abstract: Systems and methods provide for generating glyph initiations using a generative font system. A glyph variant may be generated based on an input vector glyph. A plurality of line segments may be approximated using a differentiable rasterizer with the plurality of line segments representing the contours of the glyph variant. A bitmap of the glyph variant may then be generated based on the line segments. The image loss between the bitmap and a rasterized representation of a vector glyph may be calculated and provided to the generative font system. Based on the image loss, a refined glyph variant may be provided to a user.
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公开(公告)号：US20210248376A1
公开(公告)日：2021-08-12
申请号：US16784005
申请日：2020-02-06
Applicant: Adobe Inc.
Inventor： Wentian Zhao , Seokhwan Kim , Ning Xu , Hailin Jin
Abstract: The present disclosure relates to systems, methods, and non-transitory computer-readable media for generating a response to a question received from a user during display or playback of a video segment by utilizing a query-response-neural network. The disclosed systems can extract a query vector from a question corresponding to the video segment using the query-response-neural network. The disclosed systems further generate context vectors representing both visual cues and transcript cues corresponding to the video segment using context encoders or other layers from the query-response-neural network. By utilizing additional layers from the query-response-neural network, the disclosed systems generate (i) a query-context vector based on the query vector and the context vectors, and (ii) candidate-response vectors representing candidate responses to the question from a domain-knowledge base or other source. To respond to a user's question, the disclosed systems further select a response from the candidate responses based on a comparison of the query-context vector and the candidate-response vectors.
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公开(公告)号：US11042798B2
公开(公告)日：2021-06-22
申请号：US15082877
申请日：2016-03-28
Applicant: Adobe Inc.
Inventor： Zhe Lin , Jianchao Yang , Hailin Jin , Chen Fang
Abstract: Certain embodiments involve learning features of content items (e.g., images) based on web data and user behavior data. For example, a system determines latent factors from the content items based on data including a user's text query or keyword query for a content item and the user's interaction with the content items based on the query (e.g., a user's click on a content item resulting from a search using the text query). The system uses the latent factors to learn features of the content items. The system uses a previously learned feature of the content items for iterating the process of learning features of the content items to learn additional features of the content items, which improves the accuracy with which the system is used to learn other features of the content items.
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公开(公告)号：US11003831B2
公开(公告)日：2021-05-11
申请号：US15729855
申请日：2017-10-11
Applicant: Adobe Inc.
Inventor： Zhaowen Wang , Hailin Jin , Aaron Phillip Hertzmann , Shuhui Jiang
IPC: G06F40/109 , G06N3/04 , G06K9/62 , G06F9/451 , G06N3/08
Abstract: The present disclosure relates to an asymmetric font pairing system that efficiently pairs digital fonts. For example, in one or more embodiments, the asymmetric font pairing system automatically identifies and provides users with visually aesthetic font pairs for use in different sections of an electronic document. In particular, the asymmetric font pairing system learns visually aesthetic font pairs using joint symmetric and asymmetric compatibility metric learning. In addition, the asymmetric font pairing system provides compact compatibility spaces (e.g., a symmetric compatibility space and an asymmetric compatibility space) to computing devices (e.g., client devices and server devices), which enable the computing devices to quickly and efficiently provide font pairs to users.
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Patent Agency Ranking
16.发明申请公开(公告)号：US20200311186A1
公开(公告)日：2020-10-01
申请号：US16369893
申请日：2019-03-29
Applicant: Adobe Inc.
Inventor： Zhaowen Wang , Tianlang Chen , Ning Xu , Hailin Jin
Abstract: The present disclosure relates to a font retrieval system that utilizes a multi-learning framework to develop and improve tag-based font recognition using deep learning neural networks. In particular, the font retrieval system jointly utilizes a combined recognition/retrieval model to generate font affinity scores corresponding to a list of font tags. Further, based on the font affinity scores, the font retrieval system identifies one or more fonts to recommend in response to the list of font tags such that the one or more provided fonts fairly reflect each of the font tags. Indeed, the font retrieval system utilizes a trained font retrieval neural network to efficiently and accurately identify and retrieve fonts in response to a text font tag query.
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公开(公告)号：US10783408B2
公开(公告)日：2020-09-22
申请号：US15626749
申请日：2017-06-19
Applicant: Adobe Inc.
Inventor： Zhaowen Wang , Sarah Aye Kong , I-Ming Pao , Hailin Jin , Alan Lee Erickson
IPC: G06K9/68 , G06T7/11 , G06K9/18 , G06K9/66 , G06N3/02 , G06K9/62 , G06N3/08 , G06N3/04 , G06K9/46
Abstract: Systems and techniques for identification of fonts include receiving a selection of an area of an image including text, where the selection is received from within an application. The selected area of the image is input to a font matching module within the application. The font matching module identifies one or more fonts similar to the text in the selected area using a convolutional neural network. The one or more fonts similar to the text are displayed within the application and the selection and use of the one or more fonts is enabled within the application.
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公开(公告)号：US10726313B2
公开(公告)日：2020-07-28
申请号：US15957419
申请日：2018-04-19
Applicant: Adobe Inc.
Inventor： Joon-Young Lee , Hailin Jin , Fabian David Caba Heilbron
Abstract: Various embodiments describe active learning methods for training temporal action localization models used to localize actions in untrimmed videos. A trainable active learning selection function is used to select unlabeled samples that can improve the temporal action localization model the most. The select unlabeled samples are then annotated and used to retrain the temporal action localization model. In some embodiment, the trainable active learning selection function includes a trainable performance prediction model that maps a video sample and a temporal action localization model to a predicted performance improvement for the temporal action localization model.
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公开(公告)号：US10699453B2
公开(公告)日：2020-06-30
申请号：US15679602
申请日：2017-08-17
Applicant: Adobe Inc.
Inventor： Hailin Jin , John Philip Collomosse , Brian L. Price
Abstract: Techniques and systems are described for style-aware patching of a digital image in a digital medium environment. For example, a digital image creation system generates style data for a portion to be filled of a digital image, indicating a style of an area surrounding the portion. The digital image creation system also generates content data for the portion indicating content of the digital image of the area surrounding the portion. The digital image creation system selects a source digital image based on similarity of both style and content of the source digital image at a location of the patch to the style data and content data. The digital image creation system transforms the style of the source digital image based on the style data and generates the patch from the source digital image in the transformed style for incorporation into the portion to be filled of the digital image.
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公开(公告)号：US10467508B2
公开(公告)日：2019-11-05
申请号：US15962514
申请日：2018-04-25
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 , G06K9/62 , G06N3/04
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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