Path Fusion(路径融合)研究综述
Path Fusion 路径融合 - Then, we put forward a multipath fusion Mask R-CNN with double attention (DAMF Mask R-CNN) to implement the simultaneous segmentation of tooth surface and gear pitting. [1] This topological path fusion theory not only generated all of the existed serial of fractional charges in FQHE and found the exact correspondence between FQHE and integral quantum Hall effect (IQHE), but also predicted new serial of fractional charges in FQHE. [2] Additional path fusions were shown to be possible at low computational cost, opening up possibilities for further, systematic and task-specific architecture optimisation. [3] First, for enumerative parallelization, it proposes path fusion. [4] Then, multi-path fusion comprising of underexposure and contrast-enhanced inputs is used for visibility enhancement, where saturation and Laplacian contrast are measured as weight maps and constructed for Gaussian pyramids. [5] Though remarkable success has been achieved along this direction, existing meta-path-based recommendation methods face at least one of the following issues: 1) existing methods merely adopt simple meta-path fusion rules, which might be insufficient to exclude inconsistent information of different meta-paths that may hurt model performance; 2) the representative power is limited by shallow/stage-wise formulations. [6] To further improve the accuracy of one stage single shot detector (SSD), we propose a novel Multi-Path fusion Single Shot Detector (MPSSD). [7] To explore a more abundant variety of video information, it implements a three path fusion strategy in the encoder side which combines complementary features. [8] To address this issue, we propose a novel multi-scale multi-path fusion network with cross-modal interactions (MMCI), in which the traditional two-stream fusion architecture with single fusion path is advanced by diversifying the fusion path to a global reasoning one and another local capturing one and meanwhile introducing cross-modal interactions in multiple layers. [9] This paper releases a new cervical cell dataset and proposes a network named Binary Tree-like Network with Two-path Fusion Attention Feature (BTTFA). [10] In this paper, we propose a multi-path fusion network for generating high resolution height maps while preserving scene structures well. [11] In this study, we present an independent decision path fusion (IDPF) method by developing a bimodal asynchronous BCI based on electroencephalographs (EEGs) and functional near-infrared spectroscopy (fNIRS) to discriminate multiple mental states. [12]然后，我们提出了一种具有双重注意力的多路径融合Mask R-CNN（DAMF Mask R-CNN）来实现齿面和齿轮点蚀的同时分割。 [1] 这种拓扑路径融合理论不仅生成了FQHE中所有存在的分数电荷序列，发现了FQHE与积分量子霍尔效应(IQHE)的精确对应关系，而且还预测了FQHE中新的分数电荷序列。 [2] 额外的路径融合被证明可以以较低的计算成本进行，从而为进一步的、系统的和特定于任务的架构优化开辟了可能性。 [3] 首先，对于枚举并行化，它提出了路径融合。 [4] 然后，由曝光不足和对比度增强输入组成的多路径融合用于可见性增强，其中饱和度和拉普拉斯对比度被测量为权重图并为高斯金字塔构建。 [5] 尽管在这个方向上取得了显着的成功，但现有的基于元路径的推荐方法至少面临以下问题之一：1）现有方法仅采用简单的元路径融合规则，可能不足以排除不同的不一致信息。可能损害模型性能的元路径； 2) 代表性力量受到浅层/阶段性公式的限制。 [6] 为了进一步提高单级单次检测器（SSD）的精度，我们提出了一种新颖的多路径融合单次检测器（MPSSD）。 [7] 为了探索更丰富的视频信息，它在编码器端实现了三路径融合策略，结合了互补的特征。 [8] 为了解决这个问题，我们提出了一种具有跨模态交互（MMCI）的新型多尺度多路径融合网络，其中通过将融合路径多样化到全局推理来推进具有单一融合路径的传统双流融合架构一个和另一个本地捕获一个，同时在多个层中引入跨模式交互。 [9] 本文发布了一个新的宫颈细胞数据集，并提出了一种名为二叉树状网络的具有双路径融合注意力特征的网络（BTTFA）。 [10] 在本文中，我们提出了一种多路径融合网络，用于生成高分辨率高度图，同时很好地保留场景结构。 [11] 在这项研究中，我们提出了一种独立的决策路径融合 (IDPF) 方法，通过开发基于脑电图 (EEG) 和功能近红外光谱 (fNIRS) 的双峰异步 BCI 来区分多种精神状态。 [12]
path fusion network
To address this issue, we propose a novel multi-scale multi-path fusion network with cross-modal interactions (MMCI), in which the traditional two-stream fusion architecture with single fusion path is advanced by diversifying the fusion path to a global reasoning one and another local capturing one and meanwhile introducing cross-modal interactions in multiple layers. [1] In this paper, we propose a multi-path fusion network for generating high resolution height maps while preserving scene structures well. [2]为了解决这个问题，我们提出了一种具有跨模态交互（MMCI）的新型多尺度多路径融合网络，其中通过将融合路径多样化到全局推理来推进具有单一融合路径的传统双流融合架构一个和另一个本地捕获一个，同时在多个层中引入跨模式交互。 [1] 在本文中，我们提出了一种多路径融合网络，用于生成高分辨率高度图，同时很好地保留场景结构。 [2]