Invention Grant
- Patent Title: Determining fine-grain visual style similarities for digital images by extracting style embeddings disentangled from image content
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Application No.: US17025041Application Date: 2020-09-18
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Publication No.: US11709885B2Publication Date: 2023-07-25
- Inventor: John Collomosse , Zhe Lin , Saeid Motiian , Hailin Jin , Baldo Faieta , Alex Filipkowski
- Applicant: Adobe Inc.
- Applicant Address: US CA San Jose
- Assignee: Adobe Inc.
- Current Assignee: Adobe Inc.
- Current Assignee Address: US CA San Jose
- Agency: Keller Preece PLLC
- Main IPC: G06T7/00
- IPC: G06T7/00 ; G06F16/583 ; G06F16/532 ; G06N3/08 ; G06F16/535 ; G06V10/82 ; G06V20/30

Abstract:
The present disclosure relates to systems, methods, and non-transitory computer readable media for accurately and flexibly identifying digital images with similar style to a query digital image using fine-grain style determination via weakly supervised style extraction neural networks. For example, the disclosed systems can extract a style embedding from a query digital image using a style extraction neural network such as a novel two-branch autoencoder architecture or a weakly supervised discriminative neural network. The disclosed systems can generate a combined style embedding by combining complementary style embeddings from different style extraction neural networks. Moreover, the disclosed systems can search a repository of digital images to identify digital images with similar style to the query digital image. The disclosed systems can also learn parameters for one or more style extraction neural network through weakly supervised training without a specifically labeled style ontology for sample digital images.
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