Multi Level Fusion(多级融合)研究综述
Multi Level Fusion 多级融合 - A deep learning-based method is proposed to refine the images reconstructed by conventional methods, with a multi-level fusion layer added to derive the permittivity values of multiple (>2) different dielectrics in the sensing field. [1] MrsSeg first used a lightweight backbone to extract different-resolution features, then adopted a multi-resolution fusion module to fuse the local information and global information, and finally, a multi-level fusion decoder was used to aggregate and merge the features at different levels to get the desert segmentation result. [2] Our proposed Multi-Level Fusion Net focuses on extracting more effective features to overcome these disadvantages by multi-level fusion design with a new end-to-end Convolutional Neural Network (CNN) framework. [3] In this paper, we focus on the characteristics of multi-source image and propose a novel pixel-wise classification method, named deep multi-level fusion network. [4] 048) for multi-level fusion and at 30 days (P =. [5] We further propose a gated multi-level fusion (GMLF) module to selectively integrate self-attentive cross-modal features corresponding to different levels of visual features. [6] To take advantage of the complementary information of multi-scale representations, we propose a multi-level fusion method to combine the information of both the feature level and the decision level hierarchically and generate a robust diagnostic classifier based on deep learning. [7] In order to effectively fuse multi-sensor information and improve the reliability of diagnosis, a multi-level fusion dual convolution neural network (MFDCNN) for fault diagnosis of rotating machinery is proposed in this paper. [8] CNN; ii) the spatial-temporal feature fusion strategy: multi-level fusion vs. [9] Within this network, we develop a multi-level fusion module to utilize both low-level and high-level features. [10] In addition, for improving the network capability in semantic understanding, a multi-level fusion module (MLFM) is designed in the first branch to enlarge the receptive field. [11] In addition, we analyzed the patients classified into short-level (n=111) and multi-level fusion groups (n=30). [12] In the RDAB, we firstly use the local multi-level fusion module to fully extract and deeply fuse the features of the different convolution layers. [13] Specifically, we propose a novel convolutional neural network, called KerNet, containing five branches as the backbone with a multi-level fusion architecture. [14] To fully aggregate features via multi-level fusion, multi-level features extraction scheme is presented. [15] Inspired by the multimodal integration effect, we extend the attention mechanism to multi-level fusion and design a multimodal fusion unit to obtain a global representation of affective video. [16] Background The decision upper-most instrumented vertebrae (UIV) in a multi-level fusion procedure can dramatically influence outcomes of corrective spine surgery. [17] In conclusion, advanced-age affects the discharge destination after a one- or multi-level fusion and intraoperative/postoperative blood transfusion after a one-level fusion. [18] Moreover, a feature pyramid for target detection utilizing thinning U-shaped modules (TUMs) performs the multi-level fusion of the features. [19]提出了一种基于深度学习的方法来细化通过传统方法重建的图像，并添加了多级融合层以导出传感场中多个（> 2）不同电介质的介电常数值。 [1] MrsSeg首先使用轻量级主干提取不同分辨率特征，然后采用多分辨率融合模块融合局部信息和全局信息，最后采用多级融合解码器对不同层次的特征进行聚合和融合得到沙漠分割结果。 [2] 我们提出的多级融合网络侧重于通过具有新的端到端卷积神经网络 (CNN) 框架的多级融合设计来提取更有效的特征来克服这些缺点。 [3] 在本文中，我们关注多源图像的特点，提出了一种新的逐像素分类方法，称为深度多级融合网络。 [4] 048) 用于多级融合和 30 天 (P =. [5] 我们进一步提出了一个门控多级融合（GMLF）模块，以选择性地集成与不同级别的视觉特征相对应的自注意力跨模态特征。 [6] 为了利用多尺度表示的互补信息，我们提出了一种多层次融合方法，将特征层和决策层的信息分层组合，生成基于深度学习的鲁棒诊断分类器。 [7] 为了有效融合多传感器信息，提高诊断的可靠性，本文提出了一种用于旋转机械故障诊断的多级融合双卷积神经网络（MFDCNN）。 [8] 美国有线电视新闻网； ii) 时空特征融合策略：多级融合 vs. 多级融合 [9] 在这个网络中，我们开发了一个多级融合模块来利用低级和高级特征。 [10] 此外，为了提高网络在语义理解方面的能力，在第一个分支中设计了多级融合模块（MLFM）来扩大感受野。 [11] 此外，我们分析了分为短水平（n = 111）和多水平融合组（n = 30）的患者。 [12] 在RDAB中，我们首先使用局部多级融合模块来充分提取和深度融合不同卷积层的特征。 [13] 具体来说，我们提出了一种新的卷积神经网络，称为 KerNet，包含五个分支作为主干，具有多级融合架构。 [14] 为了通过多级融合充分聚合特征，提出了多级特征提取方案。 [15] 受多模态集成效应的启发，我们将注意力机制扩展到多级融合，并设计了一个多模态融合单元以获得情感视频的全局表示。 [16] 背景 在多级融合手术中决定最上端器械椎骨 (UIV) 可以显着影响矫正脊柱手术的结果。 [17] 总之，高龄影响一级或多级融合后的出院目的地以及一级融合后的术中/术后输血。 [18] 此外，利用细化 U 形模块 (TUM) 进行目标检测的特征金字塔执行特征的多级融合。 [19]