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    VISUAL GROUNDING OF SELF-SUPERVISED REPRESENTATIONS FOR MACHINE LEARNING MODELS UTILIZING DIFFERENCE ATTENTION
    11.
    发明申请
    VISUAL GROUNDING OF SELF-SUPERVISED REPRESENTATIONS FOR MACHINE LEARNING MODELS UTILIZING DIFFERENCE ATTENTION 有权

    公开(公告)号：US20240420447A1

    公开(公告)日：2024-12-19

    申请号：US18336423

    申请日：2023-06-16

    Applicant: Adobe Inc.

    Inventor： Aishwarya Agarwal ,  Srikrishna Karanam ,  Balaji Vasan Srinivasan

    IPC: G06V10/75 ,  G06V10/80

    Abstract: The present disclosure relates to systems, non-transitory computer-readable media, and methods for utilizing difference attention to evaluate and/or train machine learning models. In particular, in some embodiments, the disclosed systems generate, utilizing a machine learning model, a first feature vector from a digital image. In one or more implementations, the disclosed systems generate a masked digital image by masking a region from the digital image. Additionally, in some embodiments, the disclosed systems generate, utilizing the machine learning model, a second feature vector from the masked digital image. Moreover, in some implementations, the disclosed systems determine a difference feature vector between the first feature vector and the second feature vector. Furthermore, in some embodiments, the disclosed systems generate, from the difference feature vector, a difference attention map reflecting a visual grounding of the machine learning model relative to the region.

    AUTOMATICALLY GENERATING GRAPHIC DESIGN VARIANTS FROM INPUT TEXT
    13.
    发明公开
    AUTOMATICALLY GENERATING GRAPHIC DESIGN VARIANTS FROM INPUT TEXT 审中-公开

    公开(公告)号：US20240152695A1

    公开(公告)日：2024-05-09

    申请号：US18052693

    申请日：2022-11-04

    Applicant: ADOBE INC.

    Inventor： Tripti Shukla ,  Khyathi Vagolu ,  Sarthak Rout ,  Nakula Neeraje ,  Akhash Nakkonda Amarnath ,  Balaji Vasan Srinivasan

    IPC: G06F40/186 ,  G06F16/56 ,  G06F40/295 ,  G06F40/56

    CPC classification number: G06F40/186 ,  G06F16/56 ,  G06F40/295 ,  G06F40/56

    Abstract: Systems and methods for automatically generating graphic design documents are described. Embodiments include identifying an input text that includes a plurality of phrases; obtaining one or more images based on the input text; encoding an image of the one or more images in a vector space using a multimodal encoder to obtain a vector image representation; encoding a phrase from the plurality of phrases in the vector space using the multimodal encoder to obtain a vector text representation; selecting an image text combination including the image and the phrase by comparing the vector image representation and the vector text representation; selecting a design template from a plurality of candidate design templates based on the image text combination; and generating a document based on the design template, wherein the document includes the at least one image and the at least one phrase.

    Constructing enterprise-specific knowledge graphs
    18.
    发明授权
    Constructing enterprise-specific knowledge graphs 有权

    公开(公告)号：US10915577B2

    公开(公告)日：2021-02-09

    申请号：US15928288

    申请日：2018-03-22

    Applicant: ADOBE INC.

    Inventor： Balaji Vasan Srinivasan ,  Rajat Chaturvedi ,  Tanya Goyal ,  Paridhi Maheshwari ,  Anish Valliyath Monsy ,  Abhilasha Sancheti

    IPC: G06F16/907 ,  G06F16/901 ,  G06F16/81

    Abstract: A framework is provided for constructing enterprise-specific knowledge bases from enterprise-specific data that includes structured and unstructured data. Relationships between entities that match known relationships are identified for each of a plurality of tuples included in the structured data. Where possible, relationships between entities that match known relationships also are identified for tuples included in the unstructured data. If matching relationships between entities that cannot be identified for tuples in the unstructured data, extracted relationships are sequentially clustered to similar relationships and a relationship is assigned to the clustered tuples. An enterprise-specific knowledge graph is constructed from the structured-data-tuples and their identified relationships, the unstructured-data-tuples where the relationships could be mapped to a known relationship and their identified relationships, and the unstructured-data-tuples that could not be mapped to a known relationship and their assigned relationships. The knowledge graph is enriched with any information determined to be missing therefrom.

    Tagging documents with security policies
    19.
    发明授权
    Tagging documents with security policies 有权

    公开(公告)号：US10783262B2

    公开(公告)日：2020-09-22

    申请号：US15424527

    申请日：2017-02-03

    Applicant: ADOBE INC.

    Inventor： Tanya Goyal ,  Sanket Vaibhav Mehta ,  Balaji Vasan Srinivasan ,  Ankur Jain

    IPC: G06F21/62 ,  G06F16/35

    Abstract: Embodiments of the present invention provide systems, methods, and computer storage media directed to facilitate identification of security policies for documents. In one embodiment, content features are identified from a set of documents having assigned security policies. The content features and corresponding security policies are analyzed to generate a security policy prediction model. Such a security policy prediction model can then be used to identify a security policy relevant to a document.

    Generating a Topic-Based Summary of Textual Content
    20.
    发明申请
    Generating a Topic-Based Summary of Textual Content 审中-公开

    公开(公告)号：US20190325066A1

    公开(公告)日：2019-10-24

    申请号：US15960505

    申请日：2018-04-23

    Applicant: Adobe Inc.

    Inventor： Kundan Krishna ,  Balaji Vasan Srinivasan

    IPC: G06F17/30 ,  G06F15/18

    Abstract: A word generation model obtains textual content and a requested topic of interest, and generates a targeted summary of the textual content tuned to the topic of interest. To do so, a topic-aware encoding model encodes the textual content with a topic label corresponding to the topic of interest to generate topic-aware encoded text. A word generation model selects a next word for the topic-based summary from the topic-aware encoded text. The word generation model is trained to generate topic-based summaries using machine learning on training data including a multitude of documents, a respective summary of each document, and a respective topic of each summary. Feedback of the selected next word is provided to the word generation model. The feedback causes the word generation model to select subsequent words for the topic-based summary based on the feedback of the next selected word.

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