Cross-media scientific research resource feature extraction model training method and device and cross-media scientific research resource feature extraction method and device

A technology for model training and feature extraction, applied in neural learning methods, biological neural network models, character and pattern recognition, etc. problems, to achieve the effect of improving comprehensiveness and effectiveness, improving accuracy and applicability, and improving characterization capabilities

Pending Publication Date: 2022-07-29
BEIJING UNIV OF POSTS & TELECOMM
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Problems solved by technology

[0003] At present, in order to realize the semantic feature extraction of scientific research resource data, the existing methods usually use deep learning models to extract data features. However, although deep learning models have general feature Some methods usually only use general-purpose data to directly train the deep learning model, so that the deep learning model cannot well reflect its representation ability when faced with scientific research resource data that

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  • Cross-media scientific research resource feature extraction model training method and device and cross-media scientific research resource feature extraction method and device
  • Cross-media scientific research resource feature extraction model training method and device and cross-media scientific research resource feature extraction method and device
  • Cross-media scientific research resource feature extraction model training method and device and cross-media scientific research resource feature extraction method and device

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[0048] In order to make the objectives, technical solutions and advantages of the present application more clear, the present application will be further described in detail below with reference to the embodiments and the accompanying drawings. Here, the exemplary embodiments and descriptions of the present application are used to explain the present application, but are not intended to limit the present application.

[0049] Here, it should also be noted that, in order to avoid obscuring the present application due to unnecessary details, only the structures and / or processing steps closely related to the solution according to the present application are shown in the drawings, and the related structures and / or processing steps are omitted. Other details not relevant to this application.

[0050] It should be emphasized that the term "comprising / comprising" as used herein refers to the presence of a feature, element, step or component, but does not exclude the presence or addit...

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Abstract

The invention provides a cross-media scientific research resource feature extraction model training method, a cross-media scientific research resource feature extraction method and a cross-media scientific research resource feature extraction device. The training method comprises the following steps: acquiring scientific research resource data of different media sources based on a cross-media scientific research resource data real-time acquisition system and performing data structured processing; performing model training and optimization on the first deep learning model according to the scientific research resource text data to form a scientific research resource text semantic feature extraction model; and performing model training and optimization on the second deep learning model according to the scientific research resource image data to form a scientific research resource image semantic feature extraction model. The method can be specially suitable for semantic feature learning of the scientific research resource data, improves the reliability, convenience and effectiveness of cross-media scientific research resource data extraction, and can improve the characterization capability of a deep learning model trained by adopting the scientific research resource data during feature extraction for the scientific research resource data. And the comprehensiveness, diversity, accuracy and reliability of the extraction result are improved.

Description

technical field [0001] The present application relates to the technical field of semantic feature learning, and in particular, to a feature extraction model training method, feature extraction method and apparatus for cross-media scientific research resources. Background technique [0002] In order to improve the retrieval convenience of scientific research resource data and realize the automatic matching between scientific research resource data and social needs, it is necessary to carry out data collection and semantic feature learning training on scientific research resource data to form a database for matching scientific research resource data and retrieval keywords. Then, the deep learning model is applied to extract semantic features with a high degree of automation for scientific research resource data. [0003] At present, in order to achieve semantic feature extraction for scientific research resource data, the existing method is usually to use a deep learning model...

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Application Information

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IPC IPC(8): G06F40/211G06F40/258G06F40/284G06F40/30G06N3/04G06N3/08G06V10/82G06V30/416
CPCG06F40/211G06F40/258G06F40/284G06F40/30G06V30/416G06V10/82G06N3/08G06N3/044G06N3/045
Inventor 杜军平王本直李文玲梁美玉邵蓥侠寇菲菲
Owner BEIJING UNIV OF POSTS & TELECOMM
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