This paper proposes a zero-shot based spatial recognition AI algorithm by fusing and developing multidimensional vision identification technology adapted to the situation in large indoor and underground spaces.With th...This paper proposes a zero-shot based spatial recognition AI algorithm by fusing and developing multidimensional vision identification technology adapted to the situation in large indoor and underground spaces.With the expansion of large shopping malls and underground urban spaces(UUS),there is an increasing need for new technologies that can quickly identify complex indoor structures and changes such as relocation,remodeling,and construction for the safety and management of citizens through the provision of the up-to-date indoor 3D site maps.The proposed algorithm utilizes data collected by an unmanned robot to create a 3D site map of the up-to-date indoor site and recognizes complex indoor spaces based on zero-shot learning.This research specifically addresses two major challenges:the difficulty of detecting walls and floors due to complex patterns and the difficulty of spatial perception due to unknown obstacles.The proposed algorithm addresses the limitations of the existing foundation model,detects floors and obstacles without expensive sensors,and improves the accuracy of spatial recognition by combining floor detection,vanishing point detection,and fusion obstacle detection algorithms.The experimental results show that the algorithm effectively detects the floor and obstacles in various indoor environments,with F1 scores of 0.96 and 0.93 in the floor detection and obstacle detection experiments,respectively.展开更多
With the availability of high-performance computing technology and the development of advanced numerical simulation methods, Computational Fluid Dynamics (CFD) is becoming more and more practical and efficient in engi...With the availability of high-performance computing technology and the development of advanced numerical simulation methods, Computational Fluid Dynamics (CFD) is becoming more and more practical and efficient in engineering. As one of the high-precision representative algorithms, the high-order Discontinuous Galerkin Method (DGM) has not only attracted widespread attention from scholars in the CFD research community, but also received strong development. However, when DGM is extended to high-speed aerodynamic flow field calculations, non-physical numerical Gibbs oscillations near shock waves often significantly affect the numerical accuracy and even cause calculation failure. Data driven approaches based on machine learning techniques can be used to learn the characteristics of Gibbs noise, which motivates us to use it in high-speed DG applications. To achieve this goal, labeled data need to be generated in order to train the machine learning models. This paper proposes a new method for denoising modeling of Gibbs phenomenon using a machine learning technique, the zero-shot learning strategy, to eliminate acquiring large amounts of CFD data. The model adopts a graph convolutional network combined with graph attention mechanism to learn the denoising paradigm from synthetic Gibbs noise data and generalize to DGM numerical simulation data. Numerical simulation results show that the Gibbs denoising model proposed in this paper can suppress the numerical oscillation near shock waves in the high-order DGM. Our work automates the extension of DGM to high-speed aerodynamic flow field calculations with higher generalization and lower cost.展开更多
Zero-shot learning enables the recognition of new class samples by migrating models learned from semanticfeatures and existing sample features to things that have never been seen before. The problems of consistencyof ...Zero-shot learning enables the recognition of new class samples by migrating models learned from semanticfeatures and existing sample features to things that have never been seen before. The problems of consistencyof different types of features and domain shift problems are two of the critical issues in zero-shot learning. Toaddress both of these issues, this paper proposes a new modeling structure. The traditional approach mappedsemantic features and visual features into the same feature space;based on this, a dual discriminator approachis used in the proposed model. This dual discriminator approach can further enhance the consistency betweensemantic and visual features. At the same time, this approach can also align unseen class semantic features andtraining set samples, providing a portion of information about the unseen classes. In addition, a new feature fusionmethod is proposed in the model. This method is equivalent to adding perturbation to the seen class features,which can reduce the degree to which the classification results in the model are biased towards the seen classes.At the same time, this feature fusion method can provide part of the information of the unseen classes, improvingits classification accuracy in generalized zero-shot learning and reducing domain bias. The proposed method isvalidated and compared with othermethods on four datasets, and fromthe experimental results, it can be seen thatthe method proposed in this paper achieves promising results.展开更多
Fine-grained image classification, which aims to distinguish images with subtle distinctions, is a challenging task for two main reasons: lack of sufficient training data for every class and difficulty in learning dis...Fine-grained image classification, which aims to distinguish images with subtle distinctions, is a challenging task for two main reasons: lack of sufficient training data for every class and difficulty in learning discriminative features for representation. In this paper, to address the two issues, we propose a two-phase framework for recognizing images from unseen fine-grained classes, i.e., zeroshot fine-grained classification. In the first feature learning phase, we finetune deep convolutional neural networks using hierarchical semantic structure among fine-grained classes to extract discriminative deep visual features. Meanwhile, a domain adaptation structure is induced into deep convolutional neural networks to avoid domain shift from training data to test data. In the second label inference phase, a semantic directed graph is constructed over attributes of fine-grained classes. Based on this graph, we develop a label propagation algorithm to infer the labels of images in the unseen classes. Experimental results on two benchmark datasets demonstrate that our model outperforms the state-of-the-art zero-shot learning models. In addition, the features obtained by our feature learning model also yield significant gains when they are used by other zero-shot learning models, which shows the flexility of our model in zero-shot finegrained classification.展开更多
Deep metric learning is one of the recommended methods for the challenge of supporting few/zero-shot learning by deep networks.It depends on building a Siamese architecture of two homogeneous Convolutional Neural Netw...Deep metric learning is one of the recommended methods for the challenge of supporting few/zero-shot learning by deep networks.It depends on building a Siamese architecture of two homogeneous Convolutional Neural Networks(CNNs)for learning a distance function that can map input data from the input space to the feature space.Instead of determining the class of each sample,the Siamese architecture deals with the existence of a few training samples by deciding if the samples share the same class identity or not.The traditional structure for the Siamese architecture was built by forming two CNNs from scratch with randomly initialized weights and trained by binary cross-entropy loss.Building two CNNs from scratch is a trial and error and time-consuming phase.In addition,training with binary crossentropy loss sometimes leads to poor margins.In this paper,a novel Siamese network is proposed and applied to few/zero-shot Handwritten Character Recognition(HCR)tasks.The novelties of the proposed network are in.1)Utilizing transfer learning and using the pre-trained AlexNet as a feature extractor in the Siamese architecture.Fine-tuning a pre-trained network is typically faster and easier than building from scratch.2)Training the Siamese architecture with contrastive loss instead of the binary cross-entropy.Contrastive loss helps the network to learn a nonlinear mapping function that enables it to map the extracted features in the vector space with an optimal way.The proposed network is evaluated on the challenging Chars74K datasets by conducting two experiments.One is for testing the proposed network in few-shot learning while the other is for testing it in zero-shot learning.The recognition accuracy of the proposed network reaches to 85.6%and 82%in few-and zero-shot learning respectively.In addition,a comparison between the performance of the proposed Siamese network and the traditional Siamese CNNs is conducted.The comparison results show that the proposed network achieves higher recognition results in less time.The proposed network reduces the training time from days to hours in both experiments.展开更多
The goal of zero-shot recognition is to classify classes it has never seen before, which needs to build a bridge between seen and unseen classes through semantic embedding space. Therefore, semantic embedding space le...The goal of zero-shot recognition is to classify classes it has never seen before, which needs to build a bridge between seen and unseen classes through semantic embedding space. Therefore, semantic embedding space learning plays an important role in zero-shot recognition. Among existing works, semantic embedding space is mainly taken by user-defined attribute vectors. However, the discriminative information included in the user-defined attribute vector is limited. In this paper, we propose to learn an extra latent attribute space automatically to produce a more generalized and discriminative semantic embedded space. To prevent the bias problem, both user-defined attribute vector and latent attribute space are optimized by adversarial learning with auto-encoders. We also propose to reconstruct semantic patterns produced by explanatory graphs, which can make semantic embedding space more sensitive to usefully semantic information and less sensitive to useless information. The proposed method is evaluated on the AwA2 and CUB dataset. These results show that our proposed method achieves superior performance.展开更多
Zero-Shot object Detection(ZSD),one of the most challenging problems in the field of object detection,aims to accurately identify new categories that are not encountered during training.Recent advancements in deep lea...Zero-Shot object Detection(ZSD),one of the most challenging problems in the field of object detection,aims to accurately identify new categories that are not encountered during training.Recent advancements in deep learning and increased computational power have led to significant improvements in object detection systems,achieving high recognition accuracy on benchmark datasets.However,these systems remain limited in real-world applications due to the scarcity of labeled training samples,making it difficult to detect unseen classes.To address this,researchers have explored various approaches,yielding promising progress.This article provides a comprehensive review of the current state of ZSD,distinguishing four related methods—zero-shot,open-vocabulary,open-set,and open-world approaches—based on task objectives and data usage.We highlight representative methods,discuss the technical challenges within each framework,and summarize the commonly used evaluation metrics,benchmark datasets,and experimental results.Our review aims to offer readers a clear overview of the latest developments and performance trends in ZSD.展开更多
Large language models(LLMs)have demonstrated remarkable generalization abilities across multiple tasks in natural language processing(NLP).For multi-step reasoning tasks,chain-of-thought(CoT)prompting facilitates step...Large language models(LLMs)have demonstrated remarkable generalization abilities across multiple tasks in natural language processing(NLP).For multi-step reasoning tasks,chain-of-thought(CoT)prompting facilitates step-by-step thinking,leading to improved performance.However,despite significant advancements in LLMs,current CoT prompting performs suboptimally on smaller-scale models that have fewer parameters.Additionally,the common paradigm of few-shot CoT prompting relies on a set of manual demonstrations,with performance contingent on the quality of these annotations and varying with task-specific requirements.To address these limitations,we propose a select-and-answer prompting method(SAP)to enhance language model performance on reasoning tasks without the need for manual demonstrations.This method comprises two primary steps:guiding the model to conduct preliminary analysis and generate several candidate answers based on the prompting;allowing the model to provide final answers derived from these candidate answers.The proposed prompting strategy is evaluated across two language models of varying sizes and six datasets.On ChatGLM-6B,SAP consistently outperforms few-shot CoT across all datasets.For GPT-3.5,SAP achieves comparable performance to few-shot CoT and outperforms zero-shot CoT in most cases.These experimental results indicate that SAP can significantly improve the accuracy of language models in reasoning tasks.展开更多
This study presents a novel multimodal medical image zero-shot segmentation algorithm named the text-visual-prompt segment anything model(TV-SAM)without any manual annotations.The TV-SAM incorporates and integrates th...This study presents a novel multimodal medical image zero-shot segmentation algorithm named the text-visual-prompt segment anything model(TV-SAM)without any manual annotations.The TV-SAM incorporates and integrates the large language model GPT-4,the vision language model GLIP,and the SAM to autonomously generate descriptive text prompts and visual bounding box prompts from medical images,thereby enhancing the SAM’s capability for zero-shot segmentation.Comprehensive evaluations are implemented on seven public datasets encompassing eight imaging modalities to demonstrate that TV-SAM can effectively segment unseen targets across various modalities without additional training.TV-SAM significantly outperforms SAM AUTO(p<0.01)and GSAM(p<0.05),closely matching the performance of SAM BBOX with gold standard bounding box prompts(p=0.07),and surpasses the state-of-the-art methods on specific datasets such as ISIC(0.853 versus 0.802)and WBC(0.968 versus 0.883).The study indicates that TV-SAM serves as an effective multimodal medical image zero-shot segmentation algorithm,highlighting the significant contribution of GPT-4 to zero-shot segmentation.By integrating foundational models such as GPT-4,GLIP,and SAM,the ability to address complex problems in specialized domains can be enhanced.展开更多
目的电力设备巡检影像缺陷检测对于提高电力传输的安全性和电网运行的可靠性具有重要作用。但由于相应训练数据集的构造成本高昂,传统的监督学习方法难以适应电力设备巡检影像缺陷检测。同时电力设备巡检影像中通常含有复杂多样的背景,...目的电力设备巡检影像缺陷检测对于提高电力传输的安全性和电网运行的可靠性具有重要作用。但由于相应训练数据集的构造成本高昂,传统的监督学习方法难以适应电力设备巡检影像缺陷检测。同时电力设备巡检影像中通常含有复杂多样的背景,严重干扰了模型对缺陷的检测。方法基于视觉语言模型并结合文本提示,提出了电力设备巡检影像零样本缺陷检测模型。模型中含有多个双专家模块,在由视觉语言模型获得文本特征和视觉特征后,经多个双专家模块处理并融合,得到像素级的缺陷检测结果。同时,构建了具有像素级掩码标注的电力设备巡检影像数据集对模型性能进行全面评测。结果在本文构建的电力设备巡检影像测试数据集上与SAA+(segment any anomaly+)、AnomalyGPT、WinCLIP(window-based CLIP)、PaDiM(patch distribution modeling)和PatchCore进行比较,在像素级的缺陷分割性能表现上,AUROC(area under the receiver operating characteristic curve)平均提升18.1%,F1-max(F1 score at optimal threshold)平均提升26.1%;在图像级的缺陷分类性能表现上,AUROC平均提升20.2%,AP(average precision)平均提升10.0%。具体到数据集中的各个电力设备,模型在像素级缺陷分割性能表现上,均获得最好结果。同时进行了消融实验,证明了双专家模块对提升模型缺陷检测精度的显著效果。结论本文模型以零样本的方式,避免了构造电力设备巡检影像数据集的高昂成本。同时提出的双专家模块,使模型减少了受巡检影像复杂背景区域的干扰。展开更多
零样本图像分类解决了训练和测试数据类别不相交的问题,人类标注属性是一种常用的实现零样本图像分类的辅助知识.为协助专家设计类属性矩阵,提出了一种交互式构建方法,简化了烦琐且缺乏指导的流程.首先,通过一种基于概念的深度学习可解...零样本图像分类解决了训练和测试数据类别不相交的问题,人类标注属性是一种常用的实现零样本图像分类的辅助知识.为协助专家设计类属性矩阵,提出了一种交互式构建方法,简化了烦琐且缺乏指导的流程.首先,通过一种基于概念的深度学习可解释性方法,在训练集图像数据中提取出可理解的属性信息;然后,采用多视图协作的交互方式,探索和分析已提取属性的重要性.系统提供了全局和局部2种方式,辅助用户设计测试集数据类别的属性值;最后,通过在数据集Animals with Attributes2上进行的案例分析,以及采用李克特量表的用户评估实验,验证了设计方法的有效性和实用性,可以帮助专家用户高效且便捷地完成类属性构建工作.展开更多
基金supported by Kyonggi University Research Grant 2024.
文摘This paper proposes a zero-shot based spatial recognition AI algorithm by fusing and developing multidimensional vision identification technology adapted to the situation in large indoor and underground spaces.With the expansion of large shopping malls and underground urban spaces(UUS),there is an increasing need for new technologies that can quickly identify complex indoor structures and changes such as relocation,remodeling,and construction for the safety and management of citizens through the provision of the up-to-date indoor 3D site maps.The proposed algorithm utilizes data collected by an unmanned robot to create a 3D site map of the up-to-date indoor site and recognizes complex indoor spaces based on zero-shot learning.This research specifically addresses two major challenges:the difficulty of detecting walls and floors due to complex patterns and the difficulty of spatial perception due to unknown obstacles.The proposed algorithm addresses the limitations of the existing foundation model,detects floors and obstacles without expensive sensors,and improves the accuracy of spatial recognition by combining floor detection,vanishing point detection,and fusion obstacle detection algorithms.The experimental results show that the algorithm effectively detects the floor and obstacles in various indoor environments,with F1 scores of 0.96 and 0.93 in the floor detection and obstacle detection experiments,respectively.
基金co-supported by the Aeronautical Science Foundation of China(Nos.2018ZA52002,2019ZA052011).
文摘With the availability of high-performance computing technology and the development of advanced numerical simulation methods, Computational Fluid Dynamics (CFD) is becoming more and more practical and efficient in engineering. As one of the high-precision representative algorithms, the high-order Discontinuous Galerkin Method (DGM) has not only attracted widespread attention from scholars in the CFD research community, but also received strong development. However, when DGM is extended to high-speed aerodynamic flow field calculations, non-physical numerical Gibbs oscillations near shock waves often significantly affect the numerical accuracy and even cause calculation failure. Data driven approaches based on machine learning techniques can be used to learn the characteristics of Gibbs noise, which motivates us to use it in high-speed DG applications. To achieve this goal, labeled data need to be generated in order to train the machine learning models. This paper proposes a new method for denoising modeling of Gibbs phenomenon using a machine learning technique, the zero-shot learning strategy, to eliminate acquiring large amounts of CFD data. The model adopts a graph convolutional network combined with graph attention mechanism to learn the denoising paradigm from synthetic Gibbs noise data and generalize to DGM numerical simulation data. Numerical simulation results show that the Gibbs denoising model proposed in this paper can suppress the numerical oscillation near shock waves in the high-order DGM. Our work automates the extension of DGM to high-speed aerodynamic flow field calculations with higher generalization and lower cost.
文摘Zero-shot learning enables the recognition of new class samples by migrating models learned from semanticfeatures and existing sample features to things that have never been seen before. The problems of consistencyof different types of features and domain shift problems are two of the critical issues in zero-shot learning. Toaddress both of these issues, this paper proposes a new modeling structure. The traditional approach mappedsemantic features and visual features into the same feature space;based on this, a dual discriminator approachis used in the proposed model. This dual discriminator approach can further enhance the consistency betweensemantic and visual features. At the same time, this approach can also align unseen class semantic features andtraining set samples, providing a portion of information about the unseen classes. In addition, a new feature fusionmethod is proposed in the model. This method is equivalent to adding perturbation to the seen class features,which can reduce the degree to which the classification results in the model are biased towards the seen classes.At the same time, this feature fusion method can provide part of the information of the unseen classes, improvingits classification accuracy in generalized zero-shot learning and reducing domain bias. The proposed method isvalidated and compared with othermethods on four datasets, and fromthe experimental results, it can be seen thatthe method proposed in this paper achieves promising results.
基金supported by National Basic Research Program of China (973 Program) (No. 2015CB352502)National Nature Science Foundation of China (No. 61573026)Beijing Nature Science Foundation (No. L172037)
文摘Fine-grained image classification, which aims to distinguish images with subtle distinctions, is a challenging task for two main reasons: lack of sufficient training data for every class and difficulty in learning discriminative features for representation. In this paper, to address the two issues, we propose a two-phase framework for recognizing images from unseen fine-grained classes, i.e., zeroshot fine-grained classification. In the first feature learning phase, we finetune deep convolutional neural networks using hierarchical semantic structure among fine-grained classes to extract discriminative deep visual features. Meanwhile, a domain adaptation structure is induced into deep convolutional neural networks to avoid domain shift from training data to test data. In the second label inference phase, a semantic directed graph is constructed over attributes of fine-grained classes. Based on this graph, we develop a label propagation algorithm to infer the labels of images in the unseen classes. Experimental results on two benchmark datasets demonstrate that our model outperforms the state-of-the-art zero-shot learning models. In addition, the features obtained by our feature learning model also yield significant gains when they are used by other zero-shot learning models, which shows the flexility of our model in zero-shot finegrained classification.
文摘Deep metric learning is one of the recommended methods for the challenge of supporting few/zero-shot learning by deep networks.It depends on building a Siamese architecture of two homogeneous Convolutional Neural Networks(CNNs)for learning a distance function that can map input data from the input space to the feature space.Instead of determining the class of each sample,the Siamese architecture deals with the existence of a few training samples by deciding if the samples share the same class identity or not.The traditional structure for the Siamese architecture was built by forming two CNNs from scratch with randomly initialized weights and trained by binary cross-entropy loss.Building two CNNs from scratch is a trial and error and time-consuming phase.In addition,training with binary crossentropy loss sometimes leads to poor margins.In this paper,a novel Siamese network is proposed and applied to few/zero-shot Handwritten Character Recognition(HCR)tasks.The novelties of the proposed network are in.1)Utilizing transfer learning and using the pre-trained AlexNet as a feature extractor in the Siamese architecture.Fine-tuning a pre-trained network is typically faster and easier than building from scratch.2)Training the Siamese architecture with contrastive loss instead of the binary cross-entropy.Contrastive loss helps the network to learn a nonlinear mapping function that enables it to map the extracted features in the vector space with an optimal way.The proposed network is evaluated on the challenging Chars74K datasets by conducting two experiments.One is for testing the proposed network in few-shot learning while the other is for testing it in zero-shot learning.The recognition accuracy of the proposed network reaches to 85.6%and 82%in few-and zero-shot learning respectively.In addition,a comparison between the performance of the proposed Siamese network and the traditional Siamese CNNs is conducted.The comparison results show that the proposed network achieves higher recognition results in less time.The proposed network reduces the training time from days to hours in both experiments.
文摘The goal of zero-shot recognition is to classify classes it has never seen before, which needs to build a bridge between seen and unseen classes through semantic embedding space. Therefore, semantic embedding space learning plays an important role in zero-shot recognition. Among existing works, semantic embedding space is mainly taken by user-defined attribute vectors. However, the discriminative information included in the user-defined attribute vector is limited. In this paper, we propose to learn an extra latent attribute space automatically to produce a more generalized and discriminative semantic embedded space. To prevent the bias problem, both user-defined attribute vector and latent attribute space are optimized by adversarial learning with auto-encoders. We also propose to reconstruct semantic patterns produced by explanatory graphs, which can make semantic embedding space more sensitive to usefully semantic information and less sensitive to useless information. The proposed method is evaluated on the AwA2 and CUB dataset. These results show that our proposed method achieves superior performance.
基金supported by the National Natural Science Foundation of China(Nos.62106150 and 62272315)the Open Fund of National Engineering Laboratory for Big Data System Computing Technology(No.SZU-BDSC-OF2024-22)+1 种基金the Open Research Fund of Anhui Province Key Laboratory of Machine Vision Inspection(No.KLMVI-2023-HIT-01)the Director Fund of Guangdong Laboratory of Artificial Intelligence and Digital Economy(Shenzhen)(No.24420001).
文摘Zero-Shot object Detection(ZSD),one of the most challenging problems in the field of object detection,aims to accurately identify new categories that are not encountered during training.Recent advancements in deep learning and increased computational power have led to significant improvements in object detection systems,achieving high recognition accuracy on benchmark datasets.However,these systems remain limited in real-world applications due to the scarcity of labeled training samples,making it difficult to detect unseen classes.To address this,researchers have explored various approaches,yielding promising progress.This article provides a comprehensive review of the current state of ZSD,distinguishing four related methods—zero-shot,open-vocabulary,open-set,and open-world approaches—based on task objectives and data usage.We highlight representative methods,discuss the technical challenges within each framework,and summarize the commonly used evaluation metrics,benchmark datasets,and experimental results.Our review aims to offer readers a clear overview of the latest developments and performance trends in ZSD.
基金National Natural Science Foundation of China(No.62176052)。
文摘Large language models(LLMs)have demonstrated remarkable generalization abilities across multiple tasks in natural language processing(NLP).For multi-step reasoning tasks,chain-of-thought(CoT)prompting facilitates step-by-step thinking,leading to improved performance.However,despite significant advancements in LLMs,current CoT prompting performs suboptimally on smaller-scale models that have fewer parameters.Additionally,the common paradigm of few-shot CoT prompting relies on a set of manual demonstrations,with performance contingent on the quality of these annotations and varying with task-specific requirements.To address these limitations,we propose a select-and-answer prompting method(SAP)to enhance language model performance on reasoning tasks without the need for manual demonstrations.This method comprises two primary steps:guiding the model to conduct preliminary analysis and generate several candidate answers based on the prompting;allowing the model to provide final answers derived from these candidate answers.The proposed prompting strategy is evaluated across two language models of varying sizes and six datasets.On ChatGLM-6B,SAP consistently outperforms few-shot CoT across all datasets.For GPT-3.5,SAP achieves comparable performance to few-shot CoT and outperforms zero-shot CoT in most cases.These experimental results indicate that SAP can significantly improve the accuracy of language models in reasoning tasks.
基金supported by the National Science and Technology Major Project(No.2021YFF1201200)Chinese National Science Foundation(No.62372316)+2 种基金Sichuan Science and Technology Program(Nos.2022YFS0048,2023YFG0126,and 2024YFHZ0091)1·3·5 Project for Disciplines of Excellence,West China Hospital,Sichuan University(No.ZYYC21004)Chongqing Technology Innovation and Application Development Project(No.CSTB2022TIAD-KPX0067).
文摘This study presents a novel multimodal medical image zero-shot segmentation algorithm named the text-visual-prompt segment anything model(TV-SAM)without any manual annotations.The TV-SAM incorporates and integrates the large language model GPT-4,the vision language model GLIP,and the SAM to autonomously generate descriptive text prompts and visual bounding box prompts from medical images,thereby enhancing the SAM’s capability for zero-shot segmentation.Comprehensive evaluations are implemented on seven public datasets encompassing eight imaging modalities to demonstrate that TV-SAM can effectively segment unseen targets across various modalities without additional training.TV-SAM significantly outperforms SAM AUTO(p<0.01)and GSAM(p<0.05),closely matching the performance of SAM BBOX with gold standard bounding box prompts(p=0.07),and surpasses the state-of-the-art methods on specific datasets such as ISIC(0.853 versus 0.802)and WBC(0.968 versus 0.883).The study indicates that TV-SAM serves as an effective multimodal medical image zero-shot segmentation algorithm,highlighting the significant contribution of GPT-4 to zero-shot segmentation.By integrating foundational models such as GPT-4,GLIP,and SAM,the ability to address complex problems in specialized domains can be enhanced.
文摘目的电力设备巡检影像缺陷检测对于提高电力传输的安全性和电网运行的可靠性具有重要作用。但由于相应训练数据集的构造成本高昂,传统的监督学习方法难以适应电力设备巡检影像缺陷检测。同时电力设备巡检影像中通常含有复杂多样的背景,严重干扰了模型对缺陷的检测。方法基于视觉语言模型并结合文本提示,提出了电力设备巡检影像零样本缺陷检测模型。模型中含有多个双专家模块,在由视觉语言模型获得文本特征和视觉特征后,经多个双专家模块处理并融合,得到像素级的缺陷检测结果。同时,构建了具有像素级掩码标注的电力设备巡检影像数据集对模型性能进行全面评测。结果在本文构建的电力设备巡检影像测试数据集上与SAA+(segment any anomaly+)、AnomalyGPT、WinCLIP(window-based CLIP)、PaDiM(patch distribution modeling)和PatchCore进行比较,在像素级的缺陷分割性能表现上,AUROC(area under the receiver operating characteristic curve)平均提升18.1%,F1-max(F1 score at optimal threshold)平均提升26.1%;在图像级的缺陷分类性能表现上,AUROC平均提升20.2%,AP(average precision)平均提升10.0%。具体到数据集中的各个电力设备,模型在像素级缺陷分割性能表现上,均获得最好结果。同时进行了消融实验,证明了双专家模块对提升模型缺陷检测精度的显著效果。结论本文模型以零样本的方式,避免了构造电力设备巡检影像数据集的高昂成本。同时提出的双专家模块,使模型减少了受巡检影像复杂背景区域的干扰。
文摘零样本图像分类解决了训练和测试数据类别不相交的问题,人类标注属性是一种常用的实现零样本图像分类的辅助知识.为协助专家设计类属性矩阵,提出了一种交互式构建方法,简化了烦琐且缺乏指导的流程.首先,通过一种基于概念的深度学习可解释性方法,在训练集图像数据中提取出可理解的属性信息;然后,采用多视图协作的交互方式,探索和分析已提取属性的重要性.系统提供了全局和局部2种方式,辅助用户设计测试集数据类别的属性值;最后,通过在数据集Animals with Attributes2上进行的案例分析,以及采用李克特量表的用户评估实验,验证了设计方法的有效性和实用性,可以帮助专家用户高效且便捷地完成类属性构建工作.
