-
公开(公告)号：US20230133522A1
公开(公告)日：2023-05-04
申请号：US17513127
申请日：2021-10-28
Applicant: Adobe Inc.
Inventor： Handong Zhao , Zhe Lin , Zhaowen Wang , Zhankui He , Ajinkya Gorakhnath Kale
IPC: G06F16/245 , G06F16/248 , G06N20/00
Abstract: Digital content search techniques are described that overcome the challenges found in conventional sequence-based techniques through use of a query-aware sequential search. In one example, a search query is received and sequence input data is obtained based on the search query. The sequence input data describes a sequence of digital content and respective search queries. Embedding data is generated based on the sequence input data using an embedding module of a machine-learning model. The embedding module includes a query-aware embedding layer that generates embeddings of the sequence of digital content and respective search queries. A search result is generated referencing at least one item of digital content by processing the embedding data using at least one layer of the machine-learning model.
-
公开(公告)号：US20230116969A1
公开(公告)日：2023-04-20
申请号：US17501191
申请日：2021-10-14
Applicant: Adobe Inc.
Inventor： Handong Zhao , Zhankui He , Zhaowen Wang , Ajinkya Gorakhnath Kale , Zhe Lin
IPC: G06F16/438 , G06F16/44 , G06N3/04
Abstract: Digital content search techniques are described. In one example, the techniques are incorporated as part of a multi-head self-attention module of a transformer using machine learning. A localized self-attention module, for instance, is incorporated as part of the multi-head self-attention module that applies local constraints to the sequence. This is performable in a variety of ways. In a first instance, a model-based local encoder is used, examples of which include a fixed-depth recurrent neural network (RNN) and a convolutional network. In a second instance, a masking-based local encoder is used, examples of which include use of a fixed window, Gaussian initialization, and an adaptive predictor.
-
公开(公告)号：US20230110114A1
公开(公告)日：2023-04-13
申请号：US17499611
申请日：2021-10-12
Applicant: Adobe Inc.
Inventor： Chinthala Pradyumna Reddy , Zhifei Zhang , Matthew Fisher , Hailin Jin , Zhaowen Wang , Niloy J Mitra
Abstract: The present disclosure relates to systems, methods, and non-transitory computer-readable media for accurately and flexibly generating scalable fonts utilizing multi-implicit neural font representations. For instance, the disclosed systems combine deep learning with differentiable rasterization to generate a multi-implicit neural font representation of a glyph. For example, the disclosed systems utilize an implicit differentiable font neural network to determine a font style code for an input glyph as well as distance values for locations of the glyph to be rendered based on a glyph label and the font style code. Further, the disclosed systems rasterize the distance values utilizing a differentiable rasterization model and combines the rasterized distance values to generate a permutation-invariant version of the glyph corresponding glyph set.
-
84.发明申请公开(公告)号：US20210342697A1
公开(公告)日：2021-11-04
申请号：US17377043
申请日：2021-07-15
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.
-
85.发明授权公开(公告)号：US11100400B2
公开(公告)日：2021-08-24
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.
-
Patent Agency Ranking
公开(公告)号：US20210103783A1
公开(公告)日：2021-04-08
申请号：US17101778
申请日：2020-11-23
Applicant: Adobe Inc.
Inventor： Zhaowen Wang , Tianlang Chen , Ning Xu , Hailin Jin
IPC: G06K9/68 , G06F16/906 , G06F16/903 , G06F40/109 , G06N3/08 , G06K9/62 , G06K9/46
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.
-
公开(公告)号：US10922852B2
公开(公告)日：2021-02-16
申请号：US16539187
申请日：2019-08-13
Applicant: Adobe Inc.
Inventor： Zhili Chen , Zhaowen Wang , Rundong Wu , Jimei Yang
Abstract: Oil painting simulation techniques are disclosed which simulate painting brush strokes using a trained neural network. In some examples, a method may include inferring a new height map of existing paint on a canvas after a new painting brush stroke is applied based on a bristle trajectory map that represents the new painting brush stroke and a height map of existing paint on the canvas prior to the application of the new painting brush stroke, and generating a rendering of the new painting brush stroke based on the new height map of existing paint on the canvas after the new painting brush stroke is applied to the canvas and a color map.
-
公开(公告)号：US10885608B2
公开(公告)日：2021-01-05
申请号：US16001656
申请日：2018-06-06
Applicant: Adobe Inc.
Inventor： Zhifei Zhang , Zhe Lin , Zhaowen Wang
Abstract: In implementations of super-resolution with reference images, a super-resolution image is generated based on reference images. Reference images are not constrained to have same or similar content as a low-resolution image being super-resolved. Texture features indicating high-frequency content are extracted into texture feature maps, and patches of texture feature maps of reference images are determined based on texture feature similarity. A content feature map indicating low-frequency content of an image is adaptively fused with a swapped texture feature map including patches of reference images with a neural network based on similarity of texture features. A user interfaces allows a user to select regions of multiple reference images to use for super-resolution. Hence, a super-resolution image can be generated with rich texture details incorporated from multiple reference images, even in the absence of reference images having similar content to an image being upscaled.
-
公开(公告)号：US10825221B1
公开(公告)日：2020-11-03
申请号：US16392041
申请日：2019-04-23
Applicant: ADOBE INC.
Inventor： Zhaowen Wang , Yipin Zhou , Trung Bui , Chen Fang
Abstract: The present disclosure provides a method for generating a video of a body moving in synchronization with music by applying a first artificial neural network (ANN) to a sequence of samples of an audio waveform of the music to generate a first latent vector describing the waveform and a sequence of coordinates of points of body parts of the body, by applying a first stage of a second ANN to the sequence of coordinates to generate a second latent vector describing movement of the body, by applying a second stage of the second ANN to static images of a person in a plurality of different poses to generate a third latent vector describing an appearance of the person, and by applying a third stage of the second ANN to the first latent vector, the second latent vector, and the third latent vector to generate the video.
-
公开(公告)号：US20200342646A1
公开(公告)日：2020-10-29
申请号：US16392041
申请日：2019-04-23
Applicant: ADOBE INC.
Inventor： Zhaowen Wang , Yipin Zhou , Trung Bui , Chen Fang
Abstract: The present disclosure provides a method for generating a video of a body moving in synchronization with music by applying a first artificial neural network (ANN) to a sequence of samples of an audio waveform of the music to generate a first latent vector describing the waveform and a sequence of coordinates of points of body parts of the body, by applying a first stage of a second ANN to the sequence of coordinates to generate a second latent vector describing movement of the body, by applying a second stage of the second ANN to static images of a person in a plurality of different poses to generate a third latent vector describing an appearance of the person, and by applying a third stage of the second ANN to the first latent vector, the second latent vector, and the third latent vector to generate the video.
-
-
-
-
-
-
-
-
-
Search Results
103
records
(took 0.031 seconds)
