公开(公告)号：US20240161529A1

公开(公告)日：2024-05-16

申请号：US18055752

申请日：2022-11-15

Applicant: Adobe Inc.

Inventor： Vlad Morariu ,  Puneet Mathur ,  Rajiv Jain ,  Ashutosh Mehra ,  Jiuxiang Gu ,  Franck Dernoncourt ,  Anandhavelu N ,  Quan Tran ,  Verena Kaynig-Fittkau ,  Nedim Lipka ,  Ani Nenkova

IPC: G06V30/413 ,  G06V10/82

CPC classification number: G06V30/413 ,  G06V10/82

Abstract: The present disclosure relates to systems, methods, and non-transitory computer-readable media that generate a digital document hierarchy comprising layers of parent-child element relationships from the visual elements. For example, for a layer of the layers, the disclosed systems determine, from the visual elements, candidate parent visual elements and child visual elements. In addition, for the layer of the layers, the disclosed systems generate, from the feature embeddings utilizing a neural network, element classifications for the candidate parent visual elements and parent-child element link probabilities for the candidate parent visual elements and the child visual elements. Moreover, for the layer, the disclosed systems select parent visual elements from the candidate parent visual elements based on the parent-child element link probabilities. Further, the disclosed systems utilize the digital document hierarchy to generate an interactive digital document from the digital document image.

公开(公告)号：US20230409672A1

公开(公告)日：2023-12-21

申请号：US18242075

申请日：2023-09-05

Applicant: Adobe Inc.

Inventor： Rajiv Jain ,  Varun Manjunatha ,  Joseph Barrow ,  Vlad Ion Morariu ,  Franck Dernoncourt ,  Sasha Spala ,  Nicholas Miller

IPC: G06F18/214 ,  G06F40/30 ,  G06F40/117 ,  G06V30/413 ,  G06F18/21 ,  G06F18/2415

CPC classification number: G06F18/2148 ,  G06F40/30 ,  G06F40/117 ,  G06V30/413 ,  G06F18/217 ,  G06F18/2415 ,  G06V2201/10 ,  G06F16/33

Abstract: Certain embodiments involve using a machine-learning tool to generate metadata identifying segments and topics for text within a document. For instance, in some embodiments, a text processing system obtains input text and applies a segmentation-and-labeling model to the input text. The segmentation-and-labeling model is trained to generate a predicted segment for the input text using a segmentation network. The segmentation-and-labeling model is also trained to generate a topic for the predicted segment using a pooling network of the model to the predicted segment. The output of the model is usable for generating metadata identifying the predicted segment and the associated topic.

公开(公告)号：US20220382975A1

公开(公告)日：2022-12-01

申请号：US17333892

申请日：2021-05-28

Applicant: Adobe Inc.

Inventor： Jiuxiang Gu ,  Vlad Morariu ,  Varun Manjunatha ,  Tong Sun ,  Rajiv Jain ,  Peizhao Li ,  Jason Kuen ,  Handong Zhao

IPC: G06F40/279 ,  G06N3/04 ,  G06N3/08 ,  G06F16/93 ,  G06F40/30 ,  G06F40/205

Abstract: One example method involves operations for a processing device that include receiving, by a machine learning model trained to generate a search result, a search query for a text input. The machine learning model is trained by receiving pre-training data that includes multiple documents. Pre-training the machine learning model by generating, using an encoder, feature embeddings for each of the documents included in the pre-training data. The feature embeddings are generated by applying a masking function to visual and textual features in the documents. Training the machine learning model also includes generating, using the feature embeddings, output features for the documents by concatenating the feature embeddings and applying a non-linear mapping to the feature embeddings. Training the machine learning model further includes applying a linear classifier to the output features. Additionally, operations include generating, for display, a search result using the machine learning model based on the input.

公开(公告)号：US20200175095A1

公开(公告)日：2020-06-04

申请号：US16204918

申请日：2018-11-29

Applicant: Adobe Inc.

Inventor： Vlad Morariu ,  Rajiv Jain ,  Nishant Sankaran

IPC: G06F17/21 ,  G06K9/00 ,  G06K9/62 ,  G06N5/04

Abstract: In some embodiments, a computing system computes tags for an electronic document. The computing system identifies sets of objects for the electronic document by applying a set of object-recognition rules to the electronic document, with each object-recognition rule generating a set of identified objects. The computing system generates feature maps that represent a set of identified objects. The computing system generates a heat map that identifies attributes of the electronic document including object candidates of the electronic document by applying a page-segmentation machine-learning model to the electronic document. The computing system computes a tag by applying a fusion deep learning module to the feature map and the heat map to correlate a document object identified by the feature map with an attribute of the electronic document identified by the heat map. The computing system generates the tagged electronic document by applying the tag to the electronic document.

公开(公告)号：US12265652B2

公开(公告)日：2025-04-01

申请号：US18055673

申请日：2022-11-15

Applicant: ADOBE INC.

Inventor： Songlin He ,  Tong Sun ,  Rajiv Jain ,  Nedim Lipka ,  Curtis Wigington ,  Anindo Roy

IPC: G06F21/64

Abstract: A method includes populating a template database with templates associated with template identifiers (IDs) identifying the templates. The method also includes generating a data model that references a template within the template database, where the data model includes a template ID referencing the template in the template database, and where the template includes a parameter field. The data model further includes a template parameter to apply to the parameter field and a digital signature for at least the template ID and the template parameter. The method also includes deploying the data model within a distributed ledger.

公开(公告)号：US20250103813A1

公开(公告)日：2025-03-27

申请号：US18472746

申请日：2023-09-22

Applicant: Adobe Inc.

Inventor： Ruiyi Zhang ,  Zhendong Chu ,  Vlad Morariu ,  Tong Yu ,  Rajiv Jain ,  Nedim Lipka ,  Jiuxiang Gu

IPC: G06F40/295

Abstract: This disclosure describes one or more implementations of systems, non-transitory computer-readable media, and methods that train a named entity recognition (NER) model with noisy training data through a self-cleaning discriminator model. For example, the disclosed systems utilize a self-cleaning guided denoising framework to improve NER learning on noisy training data via a guidance training set. In one or more implementations, the disclosed systems utilize, within the denoising framework, an auxiliary discriminator model to correct noise in the noisy training data while training an NER model through the noisy training data. For example, while training the NER model to predict labels from the noisy training data, the disclosed systems utilize a discriminator model to detect noisy NER labels and reweight the noisy NER labels provided for training in the NER model.

公开(公告)号：US11899927B2

公开(公告)日：2024-02-13

申请号：US17648718

申请日：2022-01-24

Applicant: Adobe Inc.

Inventor： Christopher Alan Tensmeyer ,  Rajiv Jain ,  Curtis Michael Wigington ,  Brian Lynn Price ,  Brian Lafayette Davis

IPC: G06K9/00 ,  G06F3/04883 ,  G06N3/08 ,  G06V30/32 ,  G06V30/228 ,  G06V30/226 ,  G06N3/045 ,  G06V10/82 ,  G06V10/44

CPC classification number: G06F3/04883 ,  G06N3/045 ,  G06N3/08 ,  G06V10/454 ,  G06V10/82 ,  G06V30/228 ,  G06V30/2264 ,  G06V30/2276 ,  G06V30/347

Abstract: Techniques are provided for generating a digital image of simulated handwriting using an encoder-decoder neural network trained on images of natural handwriting samples. The simulated handwriting image can be generated based on a style of a handwriting sample and a variable length coded text input. The style represents visually distinctive characteristics of the handwriting sample, such as the shape, size, slope, and spacing of the letters, characters, or other markings in the handwriting sample. The resulting simulated handwriting image can include the text input rendered in the style of the handwriting sample. The distinctive visual appearance of the letters or words in the simulated handwriting image mimics the visual appearance of the letters or words in the handwriting sample image, whether the letters or words in the simulated handwriting image are the same as in the handwriting sample image or different from those in the handwriting sample image.

公开(公告)号：US11893345B2

公开(公告)日：2024-02-06

申请号：US17223166

申请日：2021-04-06

Applicant: ADOBE INC.

Inventor： Amir Pouran Ben Veyseh ,  Franck Dernoncourt ,  Quan Tran ,  Varun Manjunatha ,  Lidan Wang ,  Rajiv Jain ,  Doo Soon Kim ,  Walter Chang

IPC: G06F40/284 ,  G06F40/211 ,  G06F40/30 ,  G06N3/08 ,  G06F40/126 ,  G06N3/044 ,  G06N3/045

CPC classification number: G06F40/284 ,  G06F40/126 ,  G06F40/211 ,  G06F40/30 ,  G06N3/044 ,  G06N3/045 ,  G06N3/08

Abstract: Systems and methods for natural language processing are described. One or more embodiments of the present disclosure receive a document comprising a plurality of words organized into a plurality of sentences, the words comprising an event trigger word and an argument candidate word, generate word representation vectors for the words, generate a plurality of document structures including a semantic structure for the document based on the word representation vectors, a syntax structure representing dependency relationships between the words, and a discourse structure representing discourse information of the document based on the plurality of sentences, generate a relationship representation vector based on the document structures, and predict a relationship between the event trigger word and the argument candidate word based on the relationship representation vector.

公开(公告)号：US11709915B2

公开(公告)日：2023-07-25

申请号：US17003149

申请日：2020-08-26

Applicant: Adobe Inc.

Inventor： Pramuditha Perera ,  Vlad Morariu ,  Rajiv Jain ,  Varun Manjunatha ,  Curtis Wigington

IPC: G06F18/21 ,  G06F18/214 ,  G06F18/241 ,  G06F11/32 ,  G06N3/045

CPC classification number: G06F18/2185 ,  G06F11/327 ,  G06F18/214 ,  G06F18/241 ,  G06N3/045

Abstract: The present disclosure relates to systems, non-transitory computer-readable media, and methods for classifying an input image utilizing a classification model conditioned by a generative model and/or self-supervision. For example, the disclosed systems can utilize a generative model to generate a reconstructed image from an input image to be classified. In turn, the disclosed systems can combine the reconstructed image with the input image itself. Using the combination of the input image and the reconstructed image, the disclosed systems utilize a classification model to determine a classification for the input image. Furthermore, the disclosed systems can employ self-supervised learning to cause the classification model to learn discriminative features for better classifying images of both known classes and open-set categories.

公开(公告)号：US20220147770A1

公开(公告)日：2022-05-12

申请号：US17091403

申请日：2020-11-06

Applicant: Adobe Inc.

Inventor： Rajiv Jain ,  Varun Ion Manjunatha ,  Joseph Barrow ,  Vlad Ion Moraniu ,  Franck Dernoncourt ,  Sasha Spala ,  Nicholas Miller

IPC: G06K9/62 ,  G06K9/00 ,  G06F40/30 ,  G06F40/117

Abstract: Certain embodiments involve using a machine-learning tool to generate metadata identifying segments and topics for text within a document. For instance, in some embodiments, a text processing system obtains input text and applies a segmentation-and-labeling model to the input text. The segmentation-and-labeling model is trained to generate a predicted segment for the input text using a segmentation network. The segmentation-and-labeling model is also trained to generate a topic for the predicted segment using a pooling network of the model to the predicted segment. The output of the model is usable for generating metadata identifying the predicted segment and the associated topic.