基于自适应权重训练的视频动作识别多模态模型的跨模态学习

IF 3.2 4区计算机科学 Q2 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

Connection Science Pub Date : 2024-03-27 DOI:10.1080/09540091.2024.2325474

Qingguo Zhou, Yufeng Hou, Rui Zhou, Yan Li, JinQiang Wang, Zhen Wu, Hung-Wei Li, Tien-Hsiung Weng

引用次数: 0

摘要

典型的视频动作识别方法通常用数字或单点向量标注类别，并训练神经网络对一组固定的预定义类别进行分类，从而限制了对动作的识别。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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Cross-modal learning with multi-modal model for video action recognition based on adaptive weight training

The canonical video action recognition methods usually label categories with numbers or one-hot vectors and train neural networks to classify a fixed set of predefined categories, thereby constrain...

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来源期刊

Connection Science 工程技术-计算机：理论方法

CiteScore

6.50

自引率

39.60%

发文量

审稿时长

3 months

期刊介绍： Connection Science is an interdisciplinary journal dedicated to exploring the convergence of the analytic and synthetic sciences, including neuroscience, computational modelling, artificial intelligence, machine learning, deep learning, Database, Big Data, quantum computing, Blockchain, Zero-Knowledge, Internet of Things, Cybersecurity, and parallel and distributed computing. A strong focus is on the articles arising from connectionist, probabilistic, dynamical, or evolutionary approaches in aspects of Computer Science, applied applications, and systems-level computational subjects that seek to understand models in science and engineering.