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US Patent 11301725 Visual relationship detection method and system based on region-aware learning mechanisms

Patent 11301725 was granted and assigned to Tianjin University on April, 2022 by the United States Patent and Trademark Office.

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Is a
Patent
Patent
0

Patent attributes

Patent Applicant
Tianjin University
Tianjin University
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Current Assignee
Tianjin University
Tianjin University
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Patent Jurisdiction
United States Patent and Trademark Office
United States Patent and Trademark Office
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Patent Number
113017250
Patent Inventor Names
Weizhi Nie0
Anan Liu0
Dan Song0
Hongshuo Tian0
Ning Xu0
Date of Patent
April 12, 2022
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Patent Application Number
170072450
Date Filed
August 31, 2020
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Patent Citations
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US Patent 10642891 Graph matching by sub-graph grouping and indexing
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US Patent 10679133 Constructing and utilizing a knowledge graph for information technology infrastructure
Patent Citations Received
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US Patent 11599749 Method of and system for explainable knowledge-based visual question answering
Patent Primary Examiner
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Aaron W Carter
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CPC Code
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G06N 3/08
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G06K 9/6262
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G06K 9/629
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G06K 9/6256
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G06K 9/6296
0
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G06K 9/6277
0
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G06N 20/00
0
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G06N 3/0454
0

The present invention discloses a visual relationship detection method based on a region-aware learning mechanism, comprising: acquiring a triplet graph structure and combining features after its aggregation with neighboring nodes, using the features as nodes in a second graph structure, and connecting in accordance with equiprobable edges to form the second graph structure; combining node features of the second graph structure with features of corresponding entity object nodes in the triplet, using the combined features as a visual attention mechanism and merging internal region visual features extracted by two entity objects, and using the merged region visual features as visual features to be used in the next message propagation by corresponding entity object nodes in the triplet; and after a certain number of times of message propagations, combining the output triplet node features and the node features of the second graph structure to infer predicates between object sets.

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