In recent years,the development of deep learning has further improved hash retrieval technology.Most of the existing hashing methods currently use Convolutional Neural Networks(CNNs)and Recurrent Neural Networks(RNNs)...In recent years,the development of deep learning has further improved hash retrieval technology.Most of the existing hashing methods currently use Convolutional Neural Networks(CNNs)and Recurrent Neural Networks(RNNs)to process image and text information,respectively.This makes images or texts subject to local constraints,and inherent label matching cannot capture finegrained information,often leading to suboptimal results.Driven by the development of the transformer model,we propose a framework called ViT2CMH mainly based on the Vision Transformer to handle deep Cross-modal Hashing tasks rather than CNNs or RNNs.Specifically,we use a BERT network to extract text features and use the vision transformer as the image network of the model.Finally,the features are transformed into hash codes for efficient and fast retrieval.We conduct extensive experiments on Microsoft COCO(MS-COCO)and Flickr30K,comparing with baselines of some hashing methods and image-text matching methods,showing that our method has better performance.展开更多
当前,智能服务组合研究的重点主要集中在服务描述和服务匹配方面。文中首次将服务匹配视为一个状态描述含糊、操作定义不完备的规划问题,提出了一个基于遗传规划的服务匹配(Service Match Programming,SMP)算法和适应度评价函数。仿真...当前,智能服务组合研究的重点主要集中在服务描述和服务匹配方面。文中首次将服务匹配视为一个状态描述含糊、操作定义不完备的规划问题,提出了一个基于遗传规划的服务匹配(Service Match Programming,SMP)算法和适应度评价函数。仿真实验表明,该算法在大尺度的服务选择空间中,引入了服务的关联特性,避免了局部最优现象的出现,具有更好的寻优能力和更快的速度。展开更多
基金This work was partially supported by Science and Technology Project of Chongqing Education Commission of China(KJZD-K202200513)National Natural Science Foundation of China(61370205)+1 种基金Chongqing Normal University Fund(22XLB003)Chongqing Education Science Planning Project(2021-GX-320).
文摘In recent years,the development of deep learning has further improved hash retrieval technology.Most of the existing hashing methods currently use Convolutional Neural Networks(CNNs)and Recurrent Neural Networks(RNNs)to process image and text information,respectively.This makes images or texts subject to local constraints,and inherent label matching cannot capture finegrained information,often leading to suboptimal results.Driven by the development of the transformer model,we propose a framework called ViT2CMH mainly based on the Vision Transformer to handle deep Cross-modal Hashing tasks rather than CNNs or RNNs.Specifically,we use a BERT network to extract text features and use the vision transformer as the image network of the model.Finally,the features are transformed into hash codes for efficient and fast retrieval.We conduct extensive experiments on Microsoft COCO(MS-COCO)and Flickr30K,comparing with baselines of some hashing methods and image-text matching methods,showing that our method has better performance.
文摘当前,智能服务组合研究的重点主要集中在服务描述和服务匹配方面。文中首次将服务匹配视为一个状态描述含糊、操作定义不完备的规划问题,提出了一个基于遗传规划的服务匹配(Service Match Programming,SMP)算法和适应度评价函数。仿真实验表明,该算法在大尺度的服务选择空间中,引入了服务的关联特性,避免了局部最优现象的出现,具有更好的寻优能力和更快的速度。
