公开(公告)号：US20240419750A1

公开(公告)日：2024-12-19

申请号：US18822367

申请日：2024-09-02

Applicant: Adobe Inc.

Inventor： Zhaowen Wang ,  Yue Bai ,  John Philip Collomosse

IPC: G06F16/9537 ,  G06F40/103 ,  G06F40/30 ,  G06K15/02 ,  G06N3/08 ,  G06N20/00 ,  G06V10/82 ,  G06V30/19 ,  G06V30/412 ,  G06V30/414

Abstract: Digital content layout encoding techniques for search are described. In these techniques, a layout representation is generated (using machine learning automatically and without user intervention) that describes a layout of elements included within the digital content. In an implementation, the layout representation includes a description of both spatial and structural aspects of the elements in relation to each other. To do so, a two-pathway pipeline that is configured to model layout from both spatial and structural aspects using a spatial pathway, and a structural pathway, respectively. In one example, this is also performed through use of multi-level encoding and fusion to generate a layout representation.

公开(公告)号：US20230386208A1

公开(公告)日：2023-11-30

申请号：US17804656

申请日：2022-05-31

Applicant: ADOBE INC.

Inventor： Hailin Jin ,  Jielin Qiu ,  Zhaowen Wang ,  Trung Huu Bui ,  Franck Dernoncourt

IPC: G06V20/40 ,  G06F16/683 ,  G06V10/774 ,  G06F16/34

CPC classification number: G06V20/47 ,  G06V20/49 ,  G06F16/685 ,  G06V10/774 ,  G06F16/345

Abstract: Systems and methods for video segmentation and summarization are described. Embodiments of the present disclosure receive a video and a transcript of the video; generate visual features representing frames of the video using an image encoder; generate language features representing the transcript using a text encoder, wherein the image encoder and the text encoder are trained based on a correlation between training visual features and training language features; and segment the video into a plurality of video segments based on the visual features and the language features.

公开(公告)号：US20230359682A1

公开(公告)日：2023-11-09

申请号：US17735748

申请日：2022-05-03

Applicant: Adobe Inc.

Inventor： Zhaowen Wang ,  Yue Bai ,  John Philip Collomosse

IPC: G06F16/9537 ,  G06F40/30

CPC classification number: G06F16/9537 ,  G06F40/30 ,  G06N20/00

Abstract: Digital content layout encoding techniques for search are described. In these techniques, a layout representation is generated (using machine learning automatically and without user intervention) that describes a layout of elements included within the digital content. In an implementation, the layout representation includes a description of both spatial and structural aspects of the elements in relation to each other. To do so, a two-pathway pipeline that is configured to model layout from both spatial and structural aspects using a spatial pathway, and a structural pathway, respectively. In one example, this is also performed through use of multi-level encoding and fusion to generate a layout representation.

公开(公告)号：US11810374B2

公开(公告)日：2023-11-07

申请号：US17240097

申请日：2021-04-26

Applicant: Adobe Inc.

Inventor： Zhaowen Wang ,  Hailin Jin ,  Yang Liu

IPC: G06V20/62 ,  G06V30/148 ,  G06F18/214 ,  G06V10/764

CPC classification number: G06V20/62 ,  G06F18/214 ,  G06V10/764 ,  G06V20/63 ,  G06V30/153 ,  G06V2201/01

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.

公开(公告)号：US20230326104A1

公开(公告)日：2023-10-12

申请号：US18333766

申请日：2023-06-13

Applicant: Adobe Inc.

Inventor： Nirmal Kumawat ,  Zhaowen Wang

IPC: G06T11/20 ,  G06F40/109 ,  G06F40/166 ,  G06V30/244

CPC classification number: G06T11/203 ,  G06F40/109 ,  G06F40/166 ,  G06V30/245

Abstract: Automatic font synthesis for modifying a local font to have an appearance that is visually similar to a source font is described. A font modification system receives an electronic document including the source font together with an indication of a font descriptor for the source font. The font descriptor includes information describing various font attributes for the source font, which define a visual appearance of the source font. Using the source font descriptor, the font modification system identifies a local font that is visually similar in appearance to the source font by comparing local font descriptors to the source font descriptor. A visually similar font is then synthesized by modifying glyph outlines of the local font to achieve the visual appearance defined by the source font descriptor. The synthesized font is then used to replace the source font and output in the electronic document at the computing device.

公开(公告)号：US11776180B2

公开(公告)日：2023-10-03

申请号：US16802440

申请日：2020-02-26

Applicant: ADOBE INC.

Inventor： Ning Xu ,  Bayram Safa Cicek ,  Hailin Jin ,  Zhaowen Wang

IPC: G06N20/20 ,  G06T11/60 ,  G06N3/088 ,  G06T11/00 ,  G06F18/214 ,  G06N3/045 ,  G06V10/764 ,  G06V10/774 ,  G06V10/82 ,  G06V10/44

CPC classification number: G06T11/60 ,  G06F18/214 ,  G06N3/045 ,  G06N3/088 ,  G06T11/00 ,  G06V10/454 ,  G06V10/764 ,  G06V10/774 ,  G06V10/82 ,  G06T2210/36

Abstract: Embodiments of the present disclosure are directed towards improved models trained using unsupervised domain adaptation. In particular, a style-content adaptation system provides improved translation during unsupervised domain adaptation by controlling the alignment of conditional distributions of a model during training such that content (e.g., a class) from a target domain is correctly mapped to content (e.g., the same class) in a source domain. The style-content adaptation system improves unsupervised domain adaptation using independent control over content (e.g., related to a class) as well as style (e.g., related to a domain) to control alignment when translating between the source and target domain. This independent control over content and style can also allow for images to be generated using the style-content adaptation system that contain desired content and/or style.

公开(公告)号：US11694248B2

公开(公告)日：2023-07-04

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: G06Q30/00 ,  G06Q30/0601 ,  G06N3/08 ,  G06F16/532 ,  G06N3/088 ,  G06N3/045

CPC classification number: G06Q30/0621 ,  G06F16/532 ,  G06N3/045 ,  G06N3/08 ,  G06N3/088 ,  G06Q30/0631 ,  G06Q30/0643

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.

公开(公告)号：US11636147B2

公开(公告)日：2023-04-25

申请号：US17584962

申请日：2022-01-26

Applicant: Adobe Inc.

Inventor： Zhaowen Wang ,  Tianlang Chen ,  Ning Xu ,  Hailin Jin

IPC: G06F16/906 ,  G06F16/55 ,  G06N3/084 ,  G06F16/903 ,  G06F40/109 ,  G06V30/244 ,  G06F18/28 ,  G06F18/21 ,  G06F18/214 ,  G06F18/2415 ,  G06V30/19 ,  G06V30/226 ,  G06V10/82 ,  G06V10/44

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：US11625932B2

公开(公告)日：2023-04-11

申请号：US17007790

申请日：2020-08-31

Applicant: Adobe Inc.

Inventor： Spyridon Ampanavos ,  Paul Asente ,  Jose Ignacio Echevarria Vallespi ,  Zhaowen Wang

IPC: G06F17/00 ,  G06V30/244 ,  G06N3/04 ,  G06F40/109 ,  G06F3/0482 ,  G06N3/08 ,  G06V10/40 ,  G06V10/75 ,  G06F18/22 ,  G06F18/2137

Abstract: Utilizing a visual-feature-classification model to generate font maps that efficiently and accurately organize fonts based on visual similarities. For example, extracting features from fonts of varying styles and utilize a self-organizing map (or other visual-feature-classification model) to map extracted font features to positions within font maps. Further, magnifying areas of font maps by mapping some fonts within a bounded area to positions within a higher-resolution font map. Additionally, navigating the font map to identify visually similar fonts (e.g., fonts within a threshold similarity).

公开(公告)号：US11544831B2

公开(公告)日：2023-01-03

申请号：US16984992

申请日：2020-08-04

Applicant: Adobe Inc.

Inventor： Yilin Wang ,  Zhe Lin ,  Zhaowen Wang ,  Xin Lu ,  Xiaohui Shen ,  Chih-Yao Hsieh

IPC: G06T5/50 ,  G06K9/00 ,  G06K9/62 ,  G03B7/00

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.