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US Patent 10026020 Embedding space for images with multiple text labels

Patent 10026020 was granted and assigned to Adobe Inc. on July, 2018 by the United States Patent and Trademark Office.

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Is a
Patent
Patent

Patent attributes

Patent Applicant
Adobe Inc.
Adobe Inc.
Current Assignee
Adobe Inc.
Adobe Inc.
Patent Jurisdiction
United States Patent and Trademark Office
United States Patent and Trademark Office
Patent Number
10026020
Date of Patent
July 17, 2018
Patent Application Number
14997011
Date Filed
January 15, 2016
Patent Citations Received
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US Patent 12067500 Methods for processing a plurality of candidate annotations of a given instance of an image, and for learning parameters of a computational model
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US Patent 11853352 Method and apparatus for establishing image set for image recognition, network device, and storage medium
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US Patent 11868889 Object detection in images
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US Patent 11501071 Word and image relationships in combined vector space
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US Patent 11238362 Modeling semantic concepts in an embedding space as distributions
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US Patent 11568315 Systems and methods for learning user representations for open vocabulary data sets
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US Patent 11256918 Object detection in images
Patent Primary Examiner
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Vu Le
Patent abstract

Embedding space for images with multiple text labels is described. In the embedding space both text labels and image regions are embedded. The text labels embedded describe semantic concepts that can be exhibited in image content. The embedding space is trained to semantically relate the embedded text labels so that labels like “sun” and “sunset” are more closely related than “sun” and “bird”. Training the embedding space also includes mapping representative images, having image content which exemplifies the semantic concepts, to respective text labels. Unlike conventional techniques that embed an entire training image into the embedding space for each text label associated with the training image, the techniques described herein process a training image to generate regions that correspond to the multiple text labels. The regions of the training image are then embedded into the training space in a manner that maps the regions to the corresponding text labels.

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