ID 原文 译文
40056 针对上述问题，本文引入图像风格转换方法，提出一种基于CycleGAN的水下显著性检测网络。 In response to the above problems, this paper introduces an image style conversion method and proposes an underwater salient object detection network based on CycleGAN.
40057 网络生成器由图像风格转换子网络和显著性检测子网络构成。 The network generator is composed of an image style conversion subnetwork and a salient object detection subnetwork.
40058 首先，通过无监督的级联方式对风格转换子网络进行风格转换训练，并利用该网络对陆地图像和水下图像进行风格转换，构建训练和测试图像数据集，以解决水下显著性检测数据集不足的问题； First, the network trains the domain transform subnetwork through unsupervised cascade method, and uses the network to preform style transform on in-air and underwater images to construct training and testing datasets, so as to solve the insufficient problem of underwater salient object detection.
40059 然后，使用陆地及其风格转换后的显著性数据集对显著性检测子网络进行训练，以增强网络的特征提取能力； Then, it uses in-air and salient object detection datasets after style transformation to train the salient object detection subnetwork to enhance the feature extraction ability of the network.
40060 最后对两个图像风格的输出结果进行融合优化，以提高显著性检测网络性能。 Finally, the output results of the two image styles are fused and optimized to improve the performance of the saliency detection network.
40061 实验结果表明，本文提出的水下显著性检测网络相比于单纯的陆地和水下图像显著性检测网络，其检测平均绝对误差和F值至少分别提高了10.4%和2.4%。 Experimental results show that compared with the land and underwater salient object detection network, the mean average error(MAE) and F-measure are relatively increased at least 10.4% and 2.4%, respectively.
40062 多模态磁共振影像数据采集过程中会出现不同程度的模态数据缺失，现有的补全方法大多只针对随机缺失，无法较好地恢复条状及块状缺失。 In the process of multi-modality magnetic resonance image(MRI)data acquisition, there will be different degrees of modality data missing. However, most of the existing completion methods only aim at random missing, which cannot recover strip and block missing.
40063 针对此问题，本文提出了一种基于多向延迟嵌入的平滑张量补全算法分类框架。 Therefore, this paper proposes a classification framework of smooth tensor completion algorithm based on multi-directional delay embedding.
40064 首先，对缺失数据进行多向延迟嵌入操作，得到折叠后的张量； Firstly, the folded tensor is obtained by multi-directional delay embedding of missing data.
40065 然后通过平滑张量CP分解，得到补全的张量； Then, the completed tensor is obtained by smoothing tensor CP decomposition.Finally, the reverse operation of multi-directional delay embedding is used to obtain the completed data.