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Summary

This summary is machine-generated.

This study introduces a novel framework for organizing micro-videos using venue structure and multi-modal learning. It enables efficient online processing and categorization of short videos, addressing limited venue information during upload.

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Area of Science:

Social Media Analysis
Computer Vision
Machine Learning

Background:

Micro-videos are a dominant social media trend, requiring effective organization.
Traditional video organization methods are insufficient for short, timely micro-videos.
Limited venue information (1.22%) hinders effective micro-video organization.

Purpose of the Study:

To develop a framework for organizing micro-videos online.
To leverage venue structure and multi-modal learning for micro-video representation.
To enable efficient, incremental categorization of micro-videos.

Main Methods:

Developed a structure-guided multi-modal dictionary learning model.
Incorporated venue hierarchy and modality relatedness into the model.
Implemented an online learning algorithm for incremental model strengthening and categorization.

Main Results:

The proposed framework effectively organizes micro-videos.
The model learns concept-level representations by considering venue structure and multi-modal data.
Experiments on a real-world dataset validated the model's performance.

Conclusions:

The framework provides an effective solution for online micro-video organization.
The approach addresses the challenge of limited venue information.
The released code facilitates further research in micro-video organization.