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US Patent 10424064 Instance-level semantic segmentation system

Patent 10424064 was granted and assigned to Adobe on September, 2019 by the United States Patent and Trademark Office.

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Is a
Patent
Patent
0

Patent attributes

Patent Applicant
Adobe
Adobe
0
Current Assignee
Adobe
Adobe
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Patent Jurisdiction
United States Patent and Trademark Office
United States Patent and Trademark Office
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Patent Number
104240640
Patent Inventor Names
Scott Cohen0
Brian Price0
Jimei Yang0
Date of Patent
September 24, 2019
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Patent Application Number
152968450
Date Filed
October 18, 2016
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Patent Citations Received
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US Patent 11501525 Systems and methods for panoptic image segmentation
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US Patent 11521010 Automatically choosing data samples for annotation
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US Patent 11238374 Method for verifying training data, training system, and computer readable medium
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US Patent 10769198 Systems and methods for product identification using image analysis from image mask and trained neural network
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US Patent 11294952 Systems and methods for product identification using image analysis and trained neural network
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US Patent 11748890 Instance segmentation method and system for enhanced image, and device and medium
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US Patent 11842378 Systems and methods for product identification using image analysis and trained neural network
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US Patent 11842487 Detection model training method and apparatus, computer device and storage medium
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Patent Primary Examiner
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Phuoc Tran
0
Patent abstract

Certain aspects involve semantic segmentation of objects in a digital visual medium by determining a score for each pixel of the digital visual medium that is representative of a likelihood that each pixel corresponds to the objects associated with bounding boxes within the digital visual medium. An instance-level label that yields a label for each of the pixels of the digital visual medium corresponding to the objects is determined based, in part, on a collective probability map including the score for each pixel of the digital visual medium. In some aspects, the score for each pixel corresponding to each bounding box is determined by a prediction model trained by a neural network.

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