公开(公告)号：US12056849B2

公开(公告)日：2024-08-06

申请号：US17466711

申请日：2021-09-03

Applicant: Adobe Inc. ,  Czech Technical University in Prague

Inventor： Michal Lukác ,  Daniel Sýkora ,  David Futschik ,  Zhaowen Wang ,  Elya Shechtman

IPC: G06T5/50 ,  G06F18/214

CPC classification number: G06T5/50 ,  G06F18/214 ,  G06T2207/10016 ,  G06T2207/20081 ,  G06T2207/20084

Abstract: Embodiments are disclosed for translating an image from a source visual domain to a target visual domain. In particular, in one or more embodiments, the disclosed systems and methods comprise a training process that includes receiving a training input including a pair of keyframes and an unpaired image. The pair of keyframes represent a visual translation from a first version of an image in a source visual domain to a second version of the image in a target visual domain. The one or more embodiments further include sending the pair of keyframes and the unpaired image to an image translation network to generate a first training image and a second training image. The one or more embodiments further include training the image translation network to translate images from the source visual domain to the target visual domain based on a calculated loss using the first and second training images.

公开(公告)号：US11875435B2

公开(公告)日：2024-01-16

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

IPC: G06T11/20 ,  G06T3/40

CPC classification number: G06T11/203 ,  G06T3/40

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.

公开(公告)号：US20220295149A1

公开(公告)日：2022-09-15

申请号：US17200691

申请日：2021-03-12

Applicant: Adobe Inc.

Inventor： Handong Zhao ,  Zhankui He ,  Zhe Lin ,  Zhaowen Wang ,  Ajinkya Gorakhnath Kale

IPC: H04N21/466 ,  H04N21/4722 ,  H04N21/45 ,  G06N3/08

Abstract: A multimodal recommendation identification system analyzes data describing a sequence of past content item interactions to generate a recommendation for a content item for a user. An indication of the recommended content item is provided to a website hosting system or recommendation system so that the recommended content item is displayed or otherwise presented to the user. The multimodal recommendation identification system identifies a content item to recommend to the user by generating an encoding that encodes identifiers of the sequence of content items the user has interacted with and generating encodings that encode multimodal information for content items in the sequence of content items the user has interacted with. An aggregated information encoding for a user based on these encodings and a system analyzes the content item sequence encoding and interaction between the content item sequence encoding and the multiple modality encodings to generate the aggregated information encoding.

公开(公告)号：US20220237682A1

公开(公告)日：2022-07-28

申请号：US17159554

申请日：2021-01-27

Applicant: ADOBE INC.

Inventor： Handong Zhao ,  Zhankui He ,  Zhaowen Wang ,  Zhe Lin ,  Ajinkya Kale ,  Fengbin Chen

IPC: G06Q30/06 ,  G06N3/04 ,  G06N3/08 ,  G06K9/62

Abstract: Systems and methods for item recommendation are described. Embodiments identify a sequence of items selected by a user, embed each item of the sequence of items to produce item embeddings having a reduced number of dimensions, predict a next item based on the item embeddings using a recommendation network, wherein the recommendation network includes a sequential encoder trained based at least in part on a sampled softmax classifier, and wherein predicting the next item represents a prediction that the user will interact with the next item, and provide a recommendation to the user, wherein the recommendation includes the next item.

公开(公告)号：US20220148325A1

公开(公告)日：2022-05-12

申请号：US17584962

申请日：2022-01-26

Applicant: Adobe Inc.

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

IPC: G06V30/244 ,  G06K9/62 ,  G06F16/906 ,  G06N3/08 ,  G06F16/903 ,  G06F40/109 ,  G06V10/40

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：US11295181B2

公开(公告)日：2022-04-05

申请号：US16656132

申请日：2019-10-17

Applicant: Adobe Inc.

Inventor： Nirmal Kumawat ,  Zhaowen Wang

IPC: G06K9/68 ,  G06T11/20 ,  G06F40/109 ,  G06F40/166

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：US20220067461A1

公开(公告)日：2022-03-03

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: G06K9/68 ,  G06K9/46 ,  G06K9/62 ,  G06F40/109 ,  G06F3/0482 ,  G06N3/08 ,  G06N3/04

Abstract: The present disclosure relates to systems, methods, and non-transitory computer readable media for utilizing a visual-feature-classification model to generate font maps that efficiently and accurately organize fonts based on visual similarities. For example, the disclosed systems can extract 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, the disclosed systems can also magnify areas of font maps by mapping some fonts within a bounded area to positions within a higher-resolution font map. Additionally, the disclosed systems can navigate the font map to identify visually similar fonts (e.g., fonts within a threshold similarity).

公开(公告)号：US20210241032A1

公开(公告)日：2021-08-05

申请号：US17240097

申请日：2021-04-26

Applicant: Adobe Inc.

Inventor： Zhaowen Wang ,  Hailin Jin ,  Yang Liu

IPC: G06K9/62 ,  G06K9/32 ,  G06K9/34

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：US11036915B2

公开(公告)日：2021-06-15

申请号：US15067108

申请日：2016-03-10

Applicant: ADOBE INC.

Inventor： I-Ming Pao ,  Zhaowen Wang ,  Hailin Jin ,  Alan Lee Erickson

IPC: G06F40/109 ,  G06N3/04

Abstract: Embodiments of the present invention are directed at providing a font similarity system. In one embodiment, a new font is detected on a computing device. In response to the detection of the new font, a pre-computed font list is checked to determine whether the new font is included therein. The pre-computed font list including feature representations, generated independently of the computing device, for corresponding fonts. In response to a determination that the new font is absent from the pre-computed font list, a feature representation for the new font is generated. The generated feature representation capable of being utilized for a similarity analysis of the new font. The feature representation is then stored in a supplemental font list to enable identification of one or more fonts installed on the computing device that are similar to the new font. Other embodiments may be described and/or claimed.

公开(公告)号：US10997463B2

公开(公告)日：2021-05-04

申请号：US16184779

申请日：2018-11-08

Applicant: Adobe Inc.

Inventor： Zhaowen Wang ,  Hailin Jin ,  Yang Liu

IPC: G06K9/62 ,  G06K9/32 ,  G06K9/34

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.