Vitiligo is an autoimmune skin disorder characterized by white patches of the skin losing functional epidermal melanocytes.Progressive depigmentation of the skin impairs patients’quality of life badly.More than 80%of...Vitiligo is an autoimmune skin disorder characterized by white patches of the skin losing functional epidermal melanocytes.Progressive depigmentation of the skin impairs patients’quality of life badly.More than 80%of patients with vitiligo have bilateral symmetric patterns of depigmentation,which is classified as nonsegmental vitiligo.However,few research revealed the cellular and molecular mechanisms that orchestrate the patterned activities of cutaneous immune cells in vitiligo patients.Understanding what orchestrates the activities of cutaneous immune cells is essential for determining the therapeutic targets of the disease.展开更多
在HBIM(Historic Building Information Modeling)数据库中进行信息查询面临三个问题:一是没有普适性的规则判断建筑之间的相似性;二是未考虑建筑本身所包含的历史文化信息;三是查询文本多基于关键词,难以检索到关键词未包含的信息。针...在HBIM(Historic Building Information Modeling)数据库中进行信息查询面临三个问题:一是没有普适性的规则判断建筑之间的相似性;二是未考虑建筑本身所包含的历史文化信息;三是查询文本多基于关键词,难以检索到关键词未包含的信息。针对以上问题,提出了一种面向历史建筑的多模态检索方法,用户能通过输入图像或自然语言文本数据,检索到与输入特征相符的建筑,并以列表形式进行排序。在以图像检索建筑时,利用“dino_vit16”模型对图像进行特征提取,所提出的图像-建筑检索方法检索精度达90.08%;在文本检索建筑时则基于CLIP(Contrastive Language-Image Pre-training)模型建立图像和文本的关联,研究了图文相似度和文本相似度权重的取值,选择m=0.6,n=0.4作为权重的最佳配置。实验证明所提出的文本-建筑检索算法对于包含某种外观特征查询语句的检索效果最好,对于描述某种功能和建筑风格的查询语句检索效果最差,而当查询语句中包含4个以上的混合特征,能够描述出建筑的基本面貌时,可以准确地检索到符合条件的建筑。展开更多
This research introduces an innovative approach to image classification, by making use of Vision Transformer (ViT) architecture. In fact, Vision Transformers (ViT) have emerged as a promising option for convolutional ...This research introduces an innovative approach to image classification, by making use of Vision Transformer (ViT) architecture. In fact, Vision Transformers (ViT) have emerged as a promising option for convolutional neural networks (CNN) for image analysis tasks, offering scalability and improved performance. Vision transformer ViT models are able to capture global dependencies and link among elements of images. This leads to the enhancement of feature representation. When the ViT model is trained on different models, it demonstrates strong classification capabilities across different image categories. The ViT’s ability to process image patches directly, without relying on spatial hierarchies, streamlines the classification process and improves computational efficiency. In this research, we present a Python implementation using TensorFlow to employ the (ViT) model for image classification. Four categories of animals such as (cow, dog, horse and sheep) images will be used for classification. The (ViT) model is used to extract meaningful features from images, and a classification head is added to predict the class labels. The model is trained on the CIFAR-10 dataset and evaluated for accuracy and performance. The findings from this study will not only demonstrate the effectiveness of the Vision Transformer model in image classification tasks but also its potential as a powerful tool for solving complex visual recognition problems. This research fills existing gaps in knowledge by introducing a novel approach that challenges traditional convolutional neural networks (CNNs) in the field of computer vision. While CNNs have been the dominant architecture for image classification tasks, they have limitations in capturing long-range dependencies in image data and require hand-designed hierarchical feature extraction.展开更多
文摘Vitiligo is an autoimmune skin disorder characterized by white patches of the skin losing functional epidermal melanocytes.Progressive depigmentation of the skin impairs patients’quality of life badly.More than 80%of patients with vitiligo have bilateral symmetric patterns of depigmentation,which is classified as nonsegmental vitiligo.However,few research revealed the cellular and molecular mechanisms that orchestrate the patterned activities of cutaneous immune cells in vitiligo patients.Understanding what orchestrates the activities of cutaneous immune cells is essential for determining the therapeutic targets of the disease.
文摘在HBIM(Historic Building Information Modeling)数据库中进行信息查询面临三个问题:一是没有普适性的规则判断建筑之间的相似性;二是未考虑建筑本身所包含的历史文化信息;三是查询文本多基于关键词,难以检索到关键词未包含的信息。针对以上问题,提出了一种面向历史建筑的多模态检索方法,用户能通过输入图像或自然语言文本数据,检索到与输入特征相符的建筑,并以列表形式进行排序。在以图像检索建筑时,利用“dino_vit16”模型对图像进行特征提取,所提出的图像-建筑检索方法检索精度达90.08%;在文本检索建筑时则基于CLIP(Contrastive Language-Image Pre-training)模型建立图像和文本的关联,研究了图文相似度和文本相似度权重的取值,选择m=0.6,n=0.4作为权重的最佳配置。实验证明所提出的文本-建筑检索算法对于包含某种外观特征查询语句的检索效果最好,对于描述某种功能和建筑风格的查询语句检索效果最差,而当查询语句中包含4个以上的混合特征,能够描述出建筑的基本面貌时,可以准确地检索到符合条件的建筑。
文摘This research introduces an innovative approach to image classification, by making use of Vision Transformer (ViT) architecture. In fact, Vision Transformers (ViT) have emerged as a promising option for convolutional neural networks (CNN) for image analysis tasks, offering scalability and improved performance. Vision transformer ViT models are able to capture global dependencies and link among elements of images. This leads to the enhancement of feature representation. When the ViT model is trained on different models, it demonstrates strong classification capabilities across different image categories. The ViT’s ability to process image patches directly, without relying on spatial hierarchies, streamlines the classification process and improves computational efficiency. In this research, we present a Python implementation using TensorFlow to employ the (ViT) model for image classification. Four categories of animals such as (cow, dog, horse and sheep) images will be used for classification. The (ViT) model is used to extract meaningful features from images, and a classification head is added to predict the class labels. The model is trained on the CIFAR-10 dataset and evaluated for accuracy and performance. The findings from this study will not only demonstrate the effectiveness of the Vision Transformer model in image classification tasks but also its potential as a powerful tool for solving complex visual recognition problems. This research fills existing gaps in knowledge by introducing a novel approach that challenges traditional convolutional neural networks (CNNs) in the field of computer vision. While CNNs have been the dominant architecture for image classification tasks, they have limitations in capturing long-range dependencies in image data and require hand-designed hierarchical feature extraction.
