以Web of Science数据库1995年以来“体育人工智能”等关键词为主题的549篇文献为数据来源,利用Cite Space V软件进行可视化处理和分析,以可视化知识图谱的方式梳理了近25年体育人工智能研究的国家、学科分布、研究热点以及演化趋势,探...以Web of Science数据库1995年以来“体育人工智能”等关键词为主题的549篇文献为数据来源,利用Cite Space V软件进行可视化处理和分析,以可视化知识图谱的方式梳理了近25年体育人工智能研究的国家、学科分布、研究热点以及演化趋势,探讨其研究进展和发展方向。1)体育人工智能研究地区分布较广,其中美国、中国和德国处于领先地位。2)体育人工智能研究涉及到多个学科,主要运用和借鉴了计算机科学、工程学、体育科学等学科的研究方法和理论视角。3)关键词的频次与中心性印证了目前体育人工智能领域是以机器学习为主要方向,人工神经网络为主要算法,注重以数据挖掘为基础的实践与实证研究。4)研究热点包括基于可穿戴加速度计技术的简单活动识别与能量消耗研究;基于可穿戴式传感器的动作分析与损伤防控研究;基于卷积神经网络算法的计算机视觉场景分类研究;基于计算机视觉的体能与技战术的分析与预测;基于计算机深度学习的人体姿态识别技术。展开更多
In various fields,knowledge distillation(KD)techniques that combine vision transformers(ViTs)and convolutional neural networks(CNNs)as a hybrid teacher have shown remarkable results in classification.However,in the re...In various fields,knowledge distillation(KD)techniques that combine vision transformers(ViTs)and convolutional neural networks(CNNs)as a hybrid teacher have shown remarkable results in classification.However,in the realm of remote sensing images(RSIs),existing KD research studies are not only scarce but also lack competitiveness.This issue significantly impedes the deployment of the notable advantages of ViTs and CNNs.To tackle this,the authors introduce a novel hybrid‐model KD approach named HMKD‐Net,which comprises a CNN‐ViT ensemble teacher and a CNN student.Contrary to popular opinion,the authors posit that the sparsity in RSI data distribution limits the effectiveness and efficiency of hybrid‐model knowledge transfer.As a solution,a simple yet innovative method to handle variances during the KD phase is suggested,leading to substantial enhancements in the effectiveness and efficiency of hybrid knowledge transfer.The authors assessed the performance of HMKD‐Net on three RSI datasets.The findings indicate that HMKD‐Net significantly outperforms other cuttingedge methods while maintaining a significantly smaller size.Specifically,HMKD‐Net exceeds other KD‐based methods with a maximum accuracy improvement of 22.8%across various datasets.As ablation experiments indicated,HMKD‐Net has cut down on time expenses by about 80%in the KD process.This research study validates that the hybrid‐model KD technique can be more effective and efficient if the data distribution sparsity in RSIs is well handled.展开更多
文摘以Web of Science数据库1995年以来“体育人工智能”等关键词为主题的549篇文献为数据来源,利用Cite Space V软件进行可视化处理和分析,以可视化知识图谱的方式梳理了近25年体育人工智能研究的国家、学科分布、研究热点以及演化趋势,探讨其研究进展和发展方向。1)体育人工智能研究地区分布较广,其中美国、中国和德国处于领先地位。2)体育人工智能研究涉及到多个学科,主要运用和借鉴了计算机科学、工程学、体育科学等学科的研究方法和理论视角。3)关键词的频次与中心性印证了目前体育人工智能领域是以机器学习为主要方向,人工神经网络为主要算法,注重以数据挖掘为基础的实践与实证研究。4)研究热点包括基于可穿戴加速度计技术的简单活动识别与能量消耗研究;基于可穿戴式传感器的动作分析与损伤防控研究;基于卷积神经网络算法的计算机视觉场景分类研究;基于计算机视觉的体能与技战术的分析与预测;基于计算机深度学习的人体姿态识别技术。
基金Hunan University of Arts and Science,Grant/Award Numbers:JGYB2302Geography Subject[2022]351。
文摘In various fields,knowledge distillation(KD)techniques that combine vision transformers(ViTs)and convolutional neural networks(CNNs)as a hybrid teacher have shown remarkable results in classification.However,in the realm of remote sensing images(RSIs),existing KD research studies are not only scarce but also lack competitiveness.This issue significantly impedes the deployment of the notable advantages of ViTs and CNNs.To tackle this,the authors introduce a novel hybrid‐model KD approach named HMKD‐Net,which comprises a CNN‐ViT ensemble teacher and a CNN student.Contrary to popular opinion,the authors posit that the sparsity in RSI data distribution limits the effectiveness and efficiency of hybrid‐model knowledge transfer.As a solution,a simple yet innovative method to handle variances during the KD phase is suggested,leading to substantial enhancements in the effectiveness and efficiency of hybrid knowledge transfer.The authors assessed the performance of HMKD‐Net on three RSI datasets.The findings indicate that HMKD‐Net significantly outperforms other cuttingedge methods while maintaining a significantly smaller size.Specifically,HMKD‐Net exceeds other KD‐based methods with a maximum accuracy improvement of 22.8%across various datasets.As ablation experiments indicated,HMKD‐Net has cut down on time expenses by about 80%in the KD process.This research study validates that the hybrid‐model KD technique can be more effective and efficient if the data distribution sparsity in RSIs is well handled.
