公开(公告)号：US11232255B2

公开(公告)日：2022-01-25

申请号：US16007632

申请日：2018-06-13

Applicant: Adobe Inc.

Inventor： Franck Dernoncourt ,  Walter Chang ,  Trung Bui ,  Sean Fitzgerald ,  Sasha Spala ,  Kishore Aradhya ,  Carl Dockhorn

IPC: G06F40/169

Abstract: Systems, methods, and non-transitory computer-readable media are disclosed that collect and analyze annotation performance data to generate digital annotations for evaluating and training automatic electronic document annotation models. In particular, in one or more embodiments, the disclosed systems provide electronic documents to annotators based on annotator topic preferences. The disclosed systems then identify digital annotations and annotation performance data such as a time period spent by an annotator in generating digital annotations and annotator responses to digital annotation questions. Furthermore, in one or more embodiments, the disclosed systems utilize the identified digital annotations and the annotation performance data to generate a final set of reliable digital annotations. Additionally, in one or more embodiments, the disclosed systems provide the final set of digital annotations for utilization in training a machine learning model to generate annotations for electronic documents.

公开(公告)号：US20210050033A1

公开(公告)日：2021-02-18

申请号：US16543342

申请日：2019-08-16

Applicant: Adobe Inc.

Inventor： Trung Bui ,  Subhadeep Dey ,  Seunghyun Yoon

IPC: G10L25/63 ,  G06N3/04 ,  G10L25/30 ,  G06F17/16 ,  G06F17/18

Abstract: The present disclosure relates to systems, methods, and non-transitory computer readable media for determining speech emotion. In particular, a speech emotion recognition system generates an audio feature vector and a textual feature vector for a sequence of words. Further, the speech emotion recognition system utilizes a neural attention mechanism that intelligently blends together the audio feature vector and the textual feature vector to generate attention output. Using the attention output, which includes consideration of both audio and text modalities for speech corresponding to the sequence of words, the speech emotion recognition system can apply attention methods to one of the feature vectors to generate a hidden feature vector. Based on the hidden feature vector, the speech emotion recognition system can generate a speech emotion probability distribution of emotions among a group of candidate emotions, and then select one of the candidate emotions as corresponding to the sequence of words.

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

IPC: G06T13/20 ,  G06N3/04 ,  G06N3/08 ,  G06T7/70 ,  H04N5/265 ,  G10L25/30 ,  G06K9/00

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

IPC: G06T13/20 ,  G06N3/04 ,  G06N3/08 ,  G06T7/70 ,  G06K9/00 ,  H04N5/265 ,  G10L25/30

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：US10817713B2

公开(公告)日：2020-10-27

申请号：US16192573

申请日：2018-11-15

Applicant: Adobe Inc.

Inventor： Trung Bui ,  Zhe Lin ,  Walter Chang ,  Nham Le ,  Franck Dernoncourt

IPC: G06K9/00 ,  G06N3/04 ,  G10L15/26 ,  G10L15/25

Abstract: The present disclosure relates to systems, methods, and non-transitory computer readable media for generating modified digital images based on verbal and/or gesture input by utilizing a natural language processing neural network and one or more computer vision neural networks. The disclosed systems can receive verbal input together with gesture input. The disclosed systems can further utilize a natural language processing neural network to generate a verbal command based on verbal input. The disclosed systems can select a particular computer vision neural network based on the verbal input and/or the gesture input. The disclosed systems can apply the selected computer vision neural network to identify pixels within a digital image that correspond to an object indicated by the verbal input and/or gesture input. Utilizing the identified pixels, the disclosed systems can generate a modified digital image by performing one or more editing actions indicated by the verbal input and/or gesture input.

公开(公告)号：US20200090651A1

公开(公告)日：2020-03-19

申请号：US16133190

申请日：2018-09-17

Applicant: Adobe Inc.

Inventor： Quan Tran ,  Trung Bui ,  Hung Bui

IPC: G10L15/22 ,  G10L15/16 ,  G10L15/30 ,  G06N3/04 ,  G06N3/10

Abstract: The present disclosure relates to systems, methods, and non-transitory computer readable media for generating dialogue responses based on received utterances utilizing an independent gate context-dependent additive recurrent neural network. For example, the disclosed systems can utilize a neural network model to generate a dialogue history vector based on received utterances and can use the dialogue history vector to generate a dialogue response. The independent gate context-dependent additive recurrent neural network can remove local context to reduce computation complexity and allow for gates at all time steps to be computed in parallel. The independent gate context-dependent additive recurrent neural network maintains the sequential nature of a recurrent neural network using the hidden vector output.

公开(公告)号：US10372821B2

公开(公告)日：2019-08-06

申请号：US15462684

申请日：2017-03-17

Applicant: Adobe Inc.

Inventor： Walter Chang ,  Trung Bui ,  Pranjal Daga ,  Michael Kraley ,  Hung Bui

IPC: G06F17/22 ,  G06F17/27 ,  G06K9/00

Abstract: Certain embodiments identify a correct structured reading-order sequence of text segments extracted from a file. A probabilistic language model is generated from a large text corpus to comprise observed word sequence patterns for a given language. The language model measures whether splicing together a first text segment with another continuation text segment results in a phrase that is more likely than a phrase resulting from splicing together the first text segment with other continuation text segments. Sets of text segments, which include a first set with a first text segment and a first continuation text segment as well as a second set with the first text segment and a second continuation text segment, are provided to the probabilistic model. A score indicative of a likelihood of the set providing a correct structured reading-order sequence is obtained for each set of text segments.

公开(公告)号：US20250077775A1

公开(公告)日：2025-03-06

申请号：US18457794

申请日：2023-08-29

Applicant: Adobe Inc.

Inventor： Zhongfen Deng ,  Seunghyun Yoon ,  Trung Bui ,  Quan Tran ,  Franck Dernoncourt

IPC: G06F40/284 ,  G06F40/166 ,  G06N20/00

Abstract: The present disclosure relates to systems, non-transitory computer-readable media, and methods for generating aspect-based summaries utilizing deep learning. In particular, in one or more embodiments, the disclosed systems access a transcript comprising sentences. The disclosed systems generate, utilizing a sentence classification machine learning model, aspect labels for the sentences of the transcript. The disclosed systems organize the sentences based on the aspect labels. The disclosed systems generate, utilizing a summary machine learning model, a summary of the transcript for each aspect of the plurality of aspects from the organized sentences.

公开(公告)号：US11709873B2

公开(公告)日：2023-07-25

申请号：US16741625

申请日：2020-01-13

Applicant: Adobe Inc.

Inventor： Jinfeng Xiao ,  Lidan Wang ,  Franck Dernoncourt ,  Trung Bui ,  Tong Sun

IPC: G06F16/33 ,  G06F16/953

CPC classification number: G06F16/3347 ,  G06F16/953

Abstract: Techniques and systems are provided for predicting answers in response to one or more input queries. For instance, text from a corpus of text can be processed by a reader to generate one or multiple question and answer spaces. A question and answer space can include answerable questions and the answers associated with the questions (referred to as “question and answer pairs”). A query defining a question can be received (e.g., from a user input device) and processed by a retriever portion of the system. The retriever portion of the system can retrieve an answer to the question from the one or more pre-constructed question and answer spaces, and/or can determine an answer by comparing one or more answers retrieved from the one or more pre-constructed question and answer spaces to an answer generated by a retriever-reader system.

公开(公告)号：US11537950B2

公开(公告)日：2022-12-27

申请号：US17070568

申请日：2020-10-14

Applicant: Adobe Inc.

Inventor： Trung Bui ,  Tuan Manh Lai ,  Quan Tran ,  Doo Soon Kim

IPC: G06N20/00 ,  G06F16/248

Abstract: This disclosure describes one or more implementations of a text sequence labeling system that accurately and efficiently utilize a joint-learning self-distillation approach to improve text sequence labeling machine-learning models. For example, in various implementations, the text sequence labeling system trains a text sequence labeling machine-learning teacher model to generate text sequence labels. The text sequence labeling system then creates and trains a text sequence labeling machine-learning student model utilizing the training and the output of the teacher model. Upon the student model achieving improved results over the teacher model, the text sequence labeling system re-initializes the teacher model with the learned model parameters of the student model and repeats the above joint-learning self-distillation framework. The text sequence labeling system then utilizes a trained text sequence labeling model to generate text sequence labels from input documents.