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Multi-type ocean data-oriented cross-modal retrieval method and system

A marine data, cross-modal technology, applied in multimedia data retrieval, multimedia data query, neural learning methods, etc.

Pending Publication Date: 2020-03-24
OCEAN UNIV OF CHINA +1
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  • Abstract
  • Description
  • Claims
  • Application Information

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Problems solved by technology

[0006] The present invention proposes a cross-modal retrieval method and system for multi-type marine data to solve the problem of how to realize cross-modal data retrieval

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  • Multi-type ocean data-oriented cross-modal retrieval method and system
  • Multi-type ocean data-oriented cross-modal retrieval method and system
  • Multi-type ocean data-oriented cross-modal retrieval method and system

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Embodiment Construction

[0061] Exemplary embodiments of the present invention will now be described with reference to the drawings; however, the present invention may be embodied in many different forms and are not limited to the embodiments described herein, which are provided for the purpose of exhaustively and completely disclosing the present invention. invention and fully convey the scope of the invention to those skilled in the art. The terms used in the exemplary embodiments shown in the drawings do not limit the present invention. In the figures, the same units / elements are given the same reference numerals.

[0062] Unless otherwise specified, the terms (including scientific and technical terms) used herein have the commonly understood meanings to those skilled in the art. In addition, it can be understood that terms defined by commonly used dictionaries should be understood to have consistent meanings in the context of their related fields, and should not be understood as idealized or over...

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Abstract

The invention discloses a multi-type ocean data-oriented cross-modal retrieval method and system, and the method comprises the steps: carrying out the preprocessing of a plurality of different types of ocean source data, so as to obtain processed data; optimizing the first GAN network model to determine a first optimal GAN network model corresponding to the optimal weight; optimizing the second GAN network model to determine a second optimal GAN network model; and processing the obtained marine data to be retrieved by using the first optimal GAN network model and the second optimal GAN networkmodel so as to obtain an optimal retrieval result corresponding to the marine data to be retrieved. According to the invention, an artificial intelligence image technology and a text technology are effectively combined; the relation between texts and image features can be deeply mined, the text-based image generation and retrieval function and the image-based text generation and retrieval function are achieved, cross-modal retrieval of different modal data is achieved, and the high-performance and high-efficiency cross-modal retrieval system oriented to ocean multi-type data is established.

Description

technical field [0001] The present invention relates to the field of metrology and calibration, and more specifically, relates to a cross-modal retrieval method and system for multi-type ocean data. Background technique [0002] With the development of marine information technology and the national strategy of "Digital Ocean" and "Smart Ocean" centered on high-speed network, Internet of Things, cloud computing, artificial intelligence and other new-generation technologies in China, the amount of marine data has shown explosive growth. Including survey or observation data from ships (commercial, naval, research), moored buoy and floating buoy data, coastal station data and other coastal station data, marine data formats are diversified, and the amount of data is continuously increasing, but how to effectively Using multi-dimensional, massive and real-time monitoring marine data to obtain marine professional information is the key to tap the potential value of marine big data....

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Application Information

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IPC IPC(8): G06F16/43G06N3/04G06N3/08
CPCG06F16/43G06N3/08G06N3/045
Inventor 杨永全刘园园魏志强
Owner OCEAN UNIV OF CHINA
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