A Micro Video Classification Method and System Based on Missing Data Completion

A technology of missing data and classification methods, applied in neural learning methods, instruments, biological neural network models, etc., can solve problems such as reducing the accuracy of micro-video classification tasks, achieve strong semantic representation capabilities, and ensure accuracy

Active Publication Date: 2022-06-28
SHANDONG JIANZHU UNIV
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

This will obviously reduce the accuracy of micro-video classification tasks in real scenes

Method used

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  • A Micro Video Classification Method and System Based on Missing Data Completion
  • A Micro Video Classification Method and System Based on Missing Data Completion
  • A Micro Video Classification Method and System Based on Missing Data Completion

Examples

Experimental program
Comparison scheme
Effect test

Embodiment 1

[0059] like figure 1 As shown, this embodiment provides a micro-video classification method based on missing data completion. This embodiment uses the method applied to the server as an example for illustration. It can be understood that the method can also be applied to the terminal, and can also be applied to It includes terminals, servers and systems, and is realized through the interaction of terminals and servers. The server can be an independent physical server, a server cluster or a distributed system composed of multiple physical servers, or a cloud service, cloud database, cloud computing, cloud function, cloud storage, network server, cloud communication, intermediate Cloud servers for basic cloud computing services such as software services, domain name services, security service CDNs, and big data and artificial intelligence platforms. The terminal may be a smart phone, a tablet computer, a notebook computer, a desktop computer, a smart speaker, a smart watch, etc...

Embodiment approach

[0064] As one or more implementations, the bidirectional recurrent generative adversarial network includes: three recurrent generative adversarial networks, each recurrent generative adversarial network includes two directions, generating a second modality from a first modality and generating a second modality from a second modality The modality generates a first modality, wherein the first modality is a visual modality or a sound modality or a text modality, the second modality is a visual modality or a sound modality or a text modality, and the first modality is the same as the The second mode is not the same.

[0065] Specifically, as image 3 Shown: The features of the three modalities of the micro-video go through three groups of recurrent generative adversarial networks to generate the feature representations of other modalities respectively. Each set of recurrent generative adversarial networks includes two directions, generating modality B from modality A and generati...

Embodiment 2

[0092] This embodiment provides a micro-video classification system based on missing data completion.

[0093] A micro-video classification system based on missing data completion, including:

[0094] A classification module, which is configured to: obtain a classification result of the micro-videos with partial modal data missing based on the micro-videos with partial modal data missing, using the trained micro-video classification network;

[0095] The model building module is configured as follows: the micro-video classification network includes: based on the micro-videos with missing partial modal data, a bidirectional loop is used to generate an adversarial network to obtain the missing modalities of the micro-videos; The missing modalities of the micro-videos with modalities and complements go through the common subspace learning module to extract the visual modal semantic feature representation vector, the voice modal semantic feature representation vector and the text ...

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Abstract

The invention belongs to the technical field of micro-video classification, and provides a micro-video classification method and system based on missing data completion. The method includes, based on micro-videos with partial modal data missing, using a trained micro-video classification network to obtain the classification result of micro-videos with partial modal data missing; the micro-video classification network includes: For micro-videos with missing modal data, use a two-way loop to generate an adversarial network to obtain the missing modalities of the complementary micro-videos; pass the original modalities of the micro-videos and the missing modalities of the complementary micro-videos through the common subspace learning module to extract visual Modal semantic feature representation vector, voice modal semantic feature representation vector and text modal semantic feature representation vector; the obtained visual modal semantic feature representation vector, sound modal semantic feature representation vector and text modal semantic feature representation The vector passes through the fully connected layer to obtain the classification result of micro-videos with missing modal data.

Description

technical field [0001] The invention belongs to the technical field of micro video classification, and in particular relates to a micro video classification method and system based on missing data completion. Background technique [0002] The statements in this section merely provide background information related to the present invention and do not necessarily constitute prior art. [0003] With the birth of the concept of Web 2.0 and the rapid development of the mobile Internet, social media platforms continue to emerge and gradually expand from the original PC side to the mobile side. At the same time, the media form of micro video came into being. At present, there are many micro-video social media platforms on the market, such as Douyin, Watermelon Video, Volcano Video, Kuaishou, etc. Micro video classification plays an important role in the group display of videos and personalized recommendation for users, and is an important function of the micro video platform. ...

Claims

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G06V20/40G06V10/764G06V10/774G06V10/82G06K9/62G06N3/04G06N3/08
CPCG06N3/084G06N3/044G06F18/24G06F18/214
Inventor 郭杰马玉玲聂秀山刘萌袭肖明宁阳尹义龙
Owner SHANDONG JIANZHU UNIV
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