公开(公告)号：US20240232525A9

公开(公告)日：2024-07-11

申请号：US18048900

申请日：2022-10-24

Applicant: ADOBE INC.

Inventor： Rajiv Bhawanji Jain ,  Michelle Yuan ,  Vlad Ion Morariu ,  Ani Nenkova Nenkova ,  Smitha Bangalore Naresh ,  Nikolaos Barmpalios ,  Ruchi Deshpande ,  Ruiyi Zhang ,  Jiuxiang Gu ,  Varun Manjunatha ,  Nedim Lipka ,  Andrew Marc Greene

IPC: G06F40/20 ,  G06F40/169 ,  G06N3/08

CPC classification number: G06F40/20 ,  G06F40/169 ,  G06N3/08

Abstract: Systems and methods for document classification are described. Embodiments of the present disclosure generate classification data for a plurality of samples using a neural network trained to identify a plurality of known classes; select a set of samples for annotation from the plurality of samples using an open-set metric based on the classification data, wherein the annotation includes an unknown class; and train the neural network to identify the unknown class based on the annotation of the set of samples.

公开(公告)号：US20240135096A1

公开(公告)日：2024-04-25

申请号：US18048900

申请日：2022-10-23

Applicant: ADOBE INC.

Inventor： Rajiv Bhawanji Jain ,  Michelle Yuan ,  Vlad Ion Morariu ,  Ani Nenkova Nenkova ,  Smitha Bangalore Naresh ,  Nikolaos Barmpalios ,  Ruchi Deshpande ,  Ruiyi Zhang ,  Jiuxiang Gu ,  Varun Manjunatha ,  Nedim Lipka ,  Andrew Marc Greene

IPC: G06F40/20 ,  G06F40/169 ,  G06N3/08

CPC classification number: G06F40/20 ,  G06F40/169 ,  G06N3/08

Abstract: Systems and methods for document classification are described. Embodiments of the present disclosure generate classification data for a plurality of samples using a neural network trained to identify a plurality of known classes; select a set of samples for annotation from the plurality of samples using an open-set metric based on the classification data, wherein the annotation includes an unknown class; and train the neural network to identify the unknown class based on the annotation of the set of samples.

公开(公告)号：US11880655B2

公开(公告)日：2024-01-23

申请号：US17724349

申请日：2022-04-19

Applicant: Adobe Inc.

Inventor： Christopher Tensmeyer ,  Danilo Neves Ribeiro ,  Varun Manjunatha ,  Nedim Lipka ,  Ani Nenkova

IPC: G06F40/226 ,  G06F40/284 ,  G06F40/30 ,  G06N20/00 ,  G06F16/2453 ,  G06N20/20

CPC classification number: G06F40/284 ,  G06F16/24535 ,  G06F40/226 ,  G06N20/20

Abstract: Embodiments are disclosed for performing fact correction of natural language sentences using data tables. In particular, in one or more embodiments, the disclosed systems and methods comprise receiving an input sentence, tokenizing elements of the input sentence, and identifying, by a first machine learning model, a data table associated with the input sentence. The systems and methods further comprise a second machine learning model identifying a tokenized element of the input sentence that renders the input sentence false based on the data table and masking the tokenized element of the tokenized input sentence that renders the input sentence false. The systems and method further includes a third machine learning model predicting a new value for the masked tokenized element based on the input sentence with the masked tokenized element and the identified data table and providing an output including a modified input sentence with the new value.

公开(公告)号：US20230259708A1

公开(公告)日：2023-08-17

申请号：US17650876

申请日：2022-02-14

Applicant: ADOBE INC.

Inventor： Amir Pouran Ben Veyseh ,  Franck Dernoncourt ,  Walter W. Chang ,  Trung Huu Bui ,  Hanieh Deilamsalehy ,  Seunghyun Yoon ,  Rajiv Bhawanji Jain ,  Quan Hung Tran ,  Varun Manjunatha

IPC: G06F40/289 ,  G06F40/30 ,  G10L15/22 ,  G10L15/06 ,  G10L15/16

CPC classification number: G06F40/289 ,  G06F40/30 ,  G10L15/22 ,  G10L15/063 ,  G10L15/16 ,  G10L2015/0635

Abstract: Systems and methods for key-phrase extraction are described. The systems and methods include receiving a transcript including a text paragraph and generating key-phrase data for the text paragraph using a key-phrase extraction network. The key-phrase extraction network is trained to identify domain-relevant key-phrase data based on domain data obtained using a domain discriminator network. The systems and methods further include generating meta-data for the transcript based on the key-phrase data.

公开(公告)号：US20220414338A1

公开(公告)日：2022-12-29

申请号：US17361878

申请日：2021-06-29

Applicant: ADOBE INC.

Inventor： SANGWOO CHO ,  Franck Dernoncourt ,  Timothy Jeewun Ganter ,  Trung Huu Bui ,  Nedim Lipka ,  Varun Manjunatha ,  Walter Chang ,  Hailin Jin ,  Jonathan Brandt

IPC: G06F40/35 ,  G06F40/279

Abstract: System and methods for a text summarization system are described. In one example, a text summarization system receives an input utterance and determines whether the utterance should be included in a summary of the text. The text summarization system includes an embedding network, a convolution network, an encoding component, and a summary component. The embedding network generates a semantic embedding of an utterance. The convolution network generates a plurality of feature vectors based on the semantic embedding. The encoding component identifies a plurality of latent codes respectively corresponding to the plurality of feature vectors. The summary component identifies a prominent code among the latent codes and to select the utterance as a summary utterance based on the prominent code.

公开(公告)号：US20220067449A1

公开(公告)日：2022-03-03

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: G06K9/62 ,  G06F11/32 ,  G06N3/04

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：US11227159B2

公开(公告)日：2022-01-18

申请号：US16876540

申请日：2020-05-18

Applicant: Adobe Inc.

Inventor： Rajiv Bhawanji Jain ,  Vlad Ion Morariu ,  Vitali Petsiuk ,  Varun Manjunatha ,  Ashutosh Mehra ,  Vicente Ignacio Ordonez Roman

IPC: G06K9/00 ,  G06K9/62 ,  G06T7/70 ,  G06T11/00 ,  G06K9/46

Abstract: Introduced here are computer programs and associated computer-implemented techniques for creating visualizations to explain the outputs produced by models designed for object detection. To accomplish this, a graphics editing platform can obtain a reference output that identifies a region of pixels in a digital image that allegedly contains an object. Then, the graphics editing platform can compute the similarity between the reference output and a series of outputs generated by a model upon being applied to masked versions of the digital image. A visualization component can be produced based on the similarity.

公开(公告)号：US20210334664A1

公开(公告)日：2021-10-28

申请号：US16865605

申请日：2020-05-04

Applicant: Adobe Inc.

Inventor： Kai Li ,  Christopher Alan Tensmeyer ,  Curtis Michael Wigington ,  Handong Zhao ,  Nikolaos Barmpalios ,  Tong Sun ,  Varun Manjunatha ,  Vlad Ion Morariu

IPC: G06N3/08 ,  G06N20/10 ,  G06K9/62 ,  G06K9/00 ,  G06F17/18

Abstract: Adapting a machine learning model to process data that differs from training data used to configure the model for a specified objective is described. A domain adaptation system trains the model to process new domain data that differs from a training data domain by using the model to generate a feature representation for the new domain data, which describes different content types included in the new domain data. The domain adaptation system then generates a probability distribution for each discrete region of the new domain data, which describes a likelihood of the region including different content described by the feature representation. The probability distribution is compared to ground truth information for the new domain data to determine a loss function, which is used to refine model parameters. After determining that model outputs achieve a threshold similarity to the ground truth information, the model is output as a domain-agnostic model.