Foundation models(FMs)have rapidly evolved and have achieved signicant accomplishments in computer vision tasks.Specically,the prompt mechanism conveniently allows users to integrate image prior information into the m...Foundation models(FMs)have rapidly evolved and have achieved signicant accomplishments in computer vision tasks.Specically,the prompt mechanism conveniently allows users to integrate image prior information into the model,making it possible to apply models without any training.Therefore,we proposed a workflow based on foundation models and zero training to solve the tasks of photoacoustic(PA)image processing.We employed the Segment Anything Model(SAM)by setting simple prompts and integrating the model's outputs with prior knowledge of the imaged objects to accomplish various tasks,including:(1)removing the skin signal in three-dimensional PA image rendering;(2)dual speed-of-sound reconstruction,and(3)segmentation ofnger blood vessels.Through these demonstrations,we have concluded that FMs can be directly applied in PA imaging without the requirement for network design and training.This potentially allows for a hands-on,convenient approach to achieving efficient and accurate segmentation of PA images.This paper serves as a comprehensive tutorial,facilitating the mastery of the technique through the provision of code and sample datasets.展开更多
多标签文本分类是自然语言处理领域的重要任务之一.文本的标签语义信息与文本的文档内容有紧密的联系,而传统的多标签文本分类方法存在忽略标签的语义信息以及标签的语义信息不足等问题.针对以上问题,提出一种融合标签嵌入和知识感知的...多标签文本分类是自然语言处理领域的重要任务之一.文本的标签语义信息与文本的文档内容有紧密的联系,而传统的多标签文本分类方法存在忽略标签的语义信息以及标签的语义信息不足等问题.针对以上问题,提出一种融合标签嵌入和知识感知的多标签文本分类方法 LEKA (Label Embedding and Knowledge-Aware).该方法依赖于文档文本以及相应的多个标签,通过标签嵌入来获取与标签相关的注意力.考虑标签的语义信息,建立标签与文档内容的联系,将标签应用到文本分类中.另外,为了增强标签的语义信息,通过知识图谱嵌入引入外部感知知识,对标签文本进行语义扩展.在AAPD和RCV1-V2公开数据集上与其他分类模型进行了对比,实验结果表明,与LCFA (Label Combination and Fusion of Attentions)模型相比,LEKA的F1分别提高了3.5%和2.1%.展开更多
基金support from Strategic Project of Precision Surgery,Tsinghua UniversityInitiative Scientific Research Program,Institute for Intelligent Healthcare,Tsinghua University+5 种基金Tsinghua-Foshan Institute of Advanced ManufacturingNational Natural Science Foundation of China(61735016)Beijing Nova Program(20230484308)Young Elite Scientists Sponsorship Program by CAST(2023QNRC001)Youth Elite Program of Beijing Friendship Hospital(YYQCJH2022-9)Science and Technology Program of Beijing Tongzhou District(KJ2023CX012).
文摘Foundation models(FMs)have rapidly evolved and have achieved signicant accomplishments in computer vision tasks.Specically,the prompt mechanism conveniently allows users to integrate image prior information into the model,making it possible to apply models without any training.Therefore,we proposed a workflow based on foundation models and zero training to solve the tasks of photoacoustic(PA)image processing.We employed the Segment Anything Model(SAM)by setting simple prompts and integrating the model's outputs with prior knowledge of the imaged objects to accomplish various tasks,including:(1)removing the skin signal in three-dimensional PA image rendering;(2)dual speed-of-sound reconstruction,and(3)segmentation ofnger blood vessels.Through these demonstrations,we have concluded that FMs can be directly applied in PA imaging without the requirement for network design and training.This potentially allows for a hands-on,convenient approach to achieving efficient and accurate segmentation of PA images.This paper serves as a comprehensive tutorial,facilitating the mastery of the technique through the provision of code and sample datasets.
文摘多标签文本分类是自然语言处理领域的重要任务之一.文本的标签语义信息与文本的文档内容有紧密的联系,而传统的多标签文本分类方法存在忽略标签的语义信息以及标签的语义信息不足等问题.针对以上问题,提出一种融合标签嵌入和知识感知的多标签文本分类方法 LEKA (Label Embedding and Knowledge-Aware).该方法依赖于文档文本以及相应的多个标签,通过标签嵌入来获取与标签相关的注意力.考虑标签的语义信息,建立标签与文档内容的联系,将标签应用到文本分类中.另外,为了增强标签的语义信息,通过知识图谱嵌入引入外部感知知识,对标签文本进行语义扩展.在AAPD和RCV1-V2公开数据集上与其他分类模型进行了对比,实验结果表明,与LCFA (Label Combination and Fusion of Attentions)模型相比,LEKA的F1分别提高了3.5%和2.1%.
