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公开(公告)号：US20220129498A1
公开(公告)日：2022-04-28
申请号：US17079945
申请日：2020-10-26
Applicant: Adobe Inc.
Inventor： Manoj Kilaru , Vishwa Vinay , Vidit Jain , Shaurya Goel , Ryan A. Rossi , Pratyush Garg , Nedim Lipka , Harkanwar Singh
Abstract: In implementations of systems for generating occurrence contexts for objects in digital content collections, a computing device implements a context system to receive context request data describing an object that is depicted with additional objects in digital images of a digital content collection. The context system generates relationship embeddings for the object and each of the additional objects using a representation learning model trained to predict relationships for objects. A relationship graph is formed for the object that includes a vertex for each relationship between the object and the additional objects indicated by the relationship embeddings. The context system clusters the vertices of the relationship graph into contextual clusters that each represent an occurrence context of the object in the digital images of the digital content collection. The context system generates, for each contextual cluster, an indication of a respective occurrence context for the object for display in a user interface.
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公开(公告)号：US11836187B2
公开(公告)日：2023-12-05
申请号：US17079945
申请日：2020-10-26
Applicant: Adobe Inc.
Inventor： Manoj Kilaru , Vishwa Vinay , Vidit Jain , Shaurya Goel , Ryan A. Rossi , Pratyush Garg , Nedim Lipka , Harkanwar Singh
IPC: G06F16/55 , G06N20/00 , G06F16/583 , G06F16/54 , G06F40/47 , G06F40/30 , G06F18/214
CPC classification number: G06F16/5854 , G06F16/54 , G06F16/55 , G06F18/214 , G06F40/30 , G06F40/47 , G06N20/00
Abstract: In implementations of systems for generating occurrence contexts for objects in digital content collections, a computing device implements a context system to receive context request data describing an object that is depicted with additional objects in digital images of a digital content collection. The context system generates relationship embeddings for the object and each of the additional objects using a representation learning model trained to predict relationships for objects. A relationship graph is formed for the object that includes a vertex for each relationship between the object and the additional objects indicated by the relationship embeddings. The context system clusters the vertices of the relationship graph into contextual clusters that each represent an occurrence context of the object in the digital images of the digital content collection. The context system generates, for each contextual cluster, an indication of a respective occurrence context for the object for display in a user interface.
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公开(公告)号：US20230274310A1
公开(公告)日：2023-08-31
申请号：US17680932
申请日：2022-02-25
Applicant: ADOBE INC.
Inventor： Gaurav Sinha , Vibhor Porwal , Isha Chaudhary , Simarpreet Singh Saluja , Rashul Chutani , Shaurya Goel
CPC classification number: G06Q30/0246 , G06K9/6232 , G06K9/6256
Abstract: An analytics system jointly predicts values for multiple unobserved individual-level features using aggregate data for those features. Given a dataset, a transformation is applied to individual-level information for the dataset to generate transformed data in a higher dimensional space. Bag-wise mean embeddings are generated using the transformed data. The bag-wise mean embeddings and aggregate data for unobserved individual-level features for the dataset are used to train a model to jointly predict values for the unobserved individual-features for data instances. In particular, a given data instance can be transformed to a representation in a higher dimensional space. Given this representation, the trained model predicts values for the unobserved individual-level features for the data instance, and the data instance can be augmented with the predicted values.
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