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公开(公告)号：US20240427998A1
公开(公告)日：2024-12-26
申请号：US18339694
申请日：2023-06-22
Applicant: Adobe Inc.
Inventor： Haoliang Wang , Tong Yu , Sungchul Kim , Ruiyi Zhang , Paiheng Xu , Junda Wu , Handong Zhao , Ani Nenkova
Abstract: Contextual query generation techniques are described that enable generation of a contextual query for output to a question-answering (QA) model. A content processing system, for instance, configures a language model using in-context learning to generate queries based on semantic contexts of input documents, e.g., based on one or more linguistic cues from text of the input documents. The content processing system receives an input that includes a document having text and a reference query. The content processing system leverages the language model to generate a contextual query based on a semantic context of the text of the document and the reference query. The content processing system then outputs the contextual query and the document to a QA model. Using the QA model, the content processing system generates a response as an answer to the contextual query based on the contextual query and the document.
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2.发明公开公开(公告)号：US20230230198A1
公开(公告)日：2023-07-20
申请号：US17576091
申请日：2022-01-14
Applicant: Adobe Inc.
Inventor： Ruiyi Zhang , Yufan Zhou , Christopher Tensmeyer , Jiuxiang Gu , Tong Yu , Tong Sun
CPC classification number: G06T3/0056 , G06T11/00 , G10L15/22 , G10L15/26 , G06N3/04 , G10L2015/223
Abstract: The present disclosure relates to systems, non-transitory computer-readable media, and methods that implement a neural network framework for interactive multi-round image generation from natural language inputs. Specifically, the disclosed systems provide an intelligent framework (i.e., a text-based interactive image generation model) that facilitates a multi-round image generation and editing workflow that comports with arbitrary input text and synchronous interaction. In particular embodiments, the disclosed systems utilize natural language feedback for conditioning a generative neural network that performs text-to-image generation and text-guided image modification. For example, the disclosed systems utilize a trained model to inject textual features from natural language feedback into a unified joint embedding space for generating text-informed style vectors. In turn, the disclosed systems can generate an image with semantically meaningful features that map to the natural language feedback. Moreover, the disclosed systems can persist these semantically meaningful features throughout a refinement process and across generated images.
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公开(公告)号：US20250028751A1
公开(公告)日：2025-01-23
申请号：US18355901
申请日：2023-07-20
Applicant: Adobe Inc.
Inventor： Tong Yu , Kaige Xie , Haoliang Wang , Junda Wu , Handong Zhao , Ruiyi Zhang , Kanak Vivek Mahadik , Ani Nenkova
Abstract: Dialogue skeleton assisted prompt transfer for dialogue summarization techniques are described that support training of a language model to perform dialogue summarization in a few-shot scenario. A processing device, for instance, receives a training dataset that includes training dialogues. The processing device then generates dialogue skeletons based on the training dialogues using one or more perturbation-based probes. The processing device trains a language model using prompt transfer between a source task, e.g., dialogue state tracking, and a target task, e.g., dialogue summarization, using the dialogue skeletons as supervision. The processing device then receives an input dialogue and uses the trained language model to generate a summary of the input dialogue.
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公开(公告)号：US20230143721A1
公开(公告)日：2023-05-11
申请号：US17524282
申请日：2021-11-11
Applicant: ADOBE INC.
Inventor： Sungchul Kim , Subrata Mitra , Ruiyi Zhang , Rui Wang , Handong Zhao , Tong Yu
IPC: G06F40/295 , G06N20/00
CPC classification number: G06F40/295 , G06N20/00
Abstract: Embodiments of the technology described herein describe a machine classifier capable of continually learning new classes through a continual few-shot learning approach. A natural language processing (NLP) machine classifier may initially be trained to identify a plurality of other classes through a conventional training process. In order to learn a new class, natural-language training data for a new class is generated. The training data for the new class may be few-shot training data. The training also uses synthetic training data that represents each of the plurality of other classes. The synthetic training data may be generated through a model inversion of the original classifier. The synthetic training data and the natural-language training data are used to retrain the NLP classifier to identify text in the plurality of other classes and the new class using.
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公开(公告)号：US20240273296A1
公开(公告)日：2024-08-15
申请号：US18625884
申请日：2024-04-03
Applicant: Adobe Inc.
Inventor： Sungchul KIM , Subrata MITRA , Ruiyi Zhang , Rui Wang , Handong ZHAO , Tong YU
IPC: G06F40/295 , G06N20/00
CPC classification number: G06F40/295 , G06N20/00
Abstract: Embodiments of the technology described herein describe a machine classifier capable of continually learning new classes through a continual few-shot learning approach. A natural language processing (NLP) machine classifier may initially be trained to identify a plurality of other classes through a conventional training process. In order to learn a new class, natural-language training data for a new class is generated. The training data for the new class may be few-shot training data. The training also uses synthetic training data that represents each of the plurality of other classes. The synthetic training data may be generated through a model inversion of the original classifier. The synthetic training data and the natural-language training data are used to retrain the NLP classifier to identify text in the plurality of other classes and the new class using.
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Patent Agency Ranking
公开(公告)号：US11995403B2
公开(公告)日：2024-05-28
申请号：US17524282
申请日：2021-11-11
Applicant: ADOBE INC.
Inventor： Sungchul Kim , Subrata Mitra , Ruiyi Zhang , Rui Wang , Handong Zhao , Tong Yu
IPC: G06F40/295 , G06N20/00
CPC classification number: G06F40/295 , G06N20/00
Abstract: Embodiments of the technology described herein describe a machine classifier capable of continually learning new classes through a continual few-shot learning approach. A natural language processing (NLP) machine classifier may initially be trained to identify a plurality of other classes through a conventional training process. In order to learn a new class, natural-language training data for a new class is generated. The training data for the new class may be few-shot training data. The training also uses synthetic training data that represents each of the plurality of other classes. The synthetic training data may be generated through a model inversion of the original classifier. The synthetic training data and the natural-language training data are used to retrain the NLP classifier to identify text in the plurality of other classes and the new class using.
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公开(公告)号：US20230368003A1
公开(公告)日：2023-11-16
申请号：US17740497
申请日：2022-05-10
Applicant: ADOBE INC.
Inventor： Jiuxiang Gu , Zihan Wang , Jason Wen Yong Kuen , Handong Zhao , Vlad Ion Morariu , Ruiyi Zhang , Ani Nenkova Nenkova , Tong Sun
IPC: G06N3/04 , G06F40/284
CPC classification number: G06N3/0481 , G06F40/284
Abstract: The technology described herein is directed to an adaptive sparse attention pattern that is learned during fine-tuning and deployed in a machine-learning model. In aspects, a row or a column in an attention matrix with an importance score for a task that is above a threshold importance score is identified. The important row or the column is included in an adaptive attention pattern used with a machine-learning model having a self-attention operation. In response to an input, a task-specific inference is generated for the input using the machine-learning model with the adaptive attention pattern.
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公开(公告)号：US20250005289A1
公开(公告)日：2025-01-02
申请号：US18343389
申请日：2023-06-28
Applicant: Adobe Inc.
Inventor： Haoliang Wang , Kaige Xie , Tong Yu , Junda Wu , Handong Zhao , Ruiyi Zhang , Kanak Vivek Mahadik , Ani Nenkova
Abstract: Dialogue state aware dialogue summarization techniques are described that enable generation of dialogue summaries from target domains with limited training data. A content processing system, for instance, generates one or more clusters based on training dialogues from one or more source domains. The clusters represent domain-specific features of the training dialogues and are further based on dialogue states of the training dialogues. The content processing system trains a machine learning model to generate summaries of dialogues by using the one or more clusters as prefixes in a prefix-tuning approach. The content processing system receives an input that includes a dialogue from a target domain. The content processing system generates an input prompt based on the dialogue and the one or more clusters, and the model generates a summary of the dialogue based on the input prompt.
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公开(公告)号：US20240386621A1
公开(公告)日：2024-11-21
申请号：US18318921
申请日：2023-05-17
Applicant: Adobe Inc.
Inventor： Ruiyi Zhang , Yufan Zhou , Tong Yu , Tong Sun , Rajiv Jain , Jiuxiang Gu , Christopher Alan Tensmeyer
IPC: G06T11/00 , G06F40/40 , G06V10/74 , G06V10/774 , G06V10/82
Abstract: Techniques and systems for training and/or implementing a text-to-image generation model are provided. A pre-trained multimodal model is leveraged for avoiding slower and more labor-intensive methodologies for training a text-to-image generation model. Accordingly, images without associated text (i.e., bare images) are provided to the pre-trained multimodal model so that it can produce generated text-image pairs. The generated text-image pairs are provided to the text-to-image generation model for training and/or implementing the text-to-image generation model.
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公开(公告)号：US12148119B2
公开(公告)日：2024-11-19
申请号：US17576091
申请日：2022-01-14
Applicant: Adobe Inc.
Inventor： Ruiyi Zhang , Yufan Zhou , Christopher Tensmeyer , Jiuxiang Gu , Tong Yu , Tong Sun
Abstract: The present disclosure relates to systems, non-transitory computer-readable media, and methods that implement a neural network framework for interactive multi-round image generation from natural language inputs. Specifically, the disclosed systems provide an intelligent framework (i.e., a text-based interactive image generation model) that facilitates a multi-round image generation and editing workflow that comports with arbitrary input text and synchronous interaction. In particular embodiments, the disclosed systems utilize natural language feedback for conditioning a generative neural network that performs text-to-image generation and text-guided image modification. For example, the disclosed systems utilize a trained model to inject textual features from natural language feedback into a unified joint embedding space for generating text-informed style vectors. In turn, the disclosed systems can generate an image with semantically meaningful features that map to the natural language feedback. Moreover, the disclosed systems can persist these semantically meaningful features throughout a refinement process and across generated images.
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