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公开(公告)号:US12147909B2
公开(公告)日:2024-11-19
申请号:US18491817
申请日:2023-10-23
Applicant: ZHEJIANG LAB
Abstract: A method and an apparatus for cross-media corresponding knowledge generation. The method comprises: generating a second knowledge unit of a second medium according to a first knowledge unit of a predefined first medium; generating a first feature parameter vector corresponding to the first knowledge unit and a second feature parameter vector corresponding to the second knowledge unit; mapping the first feature parameter vector and the second feature parameter vector to a corresponding two-dimensional spherical feature surface to obtain a first feature point of the first feature parameter vector on the corresponding two-dimensional spherical feature surface and a second feature point of the second feature parameter vector on the corresponding two-dimensional spherical feature surface; indexing the first feature point and the second feature point to obtain a first index and a second index; and generating a bidirectional index corresponding relationship between the first knowledge unit and the second knowledge unit.
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公开(公告)号:US12106589B2
公开(公告)日:2024-10-01
申请号:US18491818
申请日:2023-10-23
Applicant: ZHEJIANG LAB
CPC classification number: G06V20/70 , G06F40/30 , G16H30/40 , G06V2201/03
Abstract: A cross-media knowledge semantic representation method and apparatus. The method comprises: performing data acquisition according to a preset semantic description; inputting data information of a topological structure acquired by the data acquisition into a preset stack of an automat corresponding to the semantic description, the finite state set is used for indicating states included in the automat, and the input vocabulary list is used for indicating vocabularies included in the automat; mapping the data information by the automat to obtain key frames corresponding respectively to substructures and/or branches of a target object acquired by the data acquisition; and generating a visual semantic representation of the topological structure according to the key frames corresponding respectively to the substructures and/or branches of the target object acquired by the data acquisition, such that cross-media knowledge alignment is realized.
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