本文提出Multi-Level Feature Pyramid Network来搭建高效检测不同尺度目标的特征金字塔。MLFPN由FFM、TUMs以及SFAM三部分组成。其中FFMv1(Feature Fusion Module)用于混合由backbone提取的多层级特征作为基础特征;TUMs(Thinned U-shape Modules)以及FFMv2s通过基础特征提取出多层级多尺度的特征;SFAM(Scale-wise Feature Aggr...
如Figure 2所示,我们首先将backbone提取的多级特征(即多层)融合为基础特征,然后将其输入Multi-Level Feature Pyramid Network(MLFPN)中。MLFPN包含交替连接的Thinned U-shape Modules(TUM)、Feature Fusion Module(FFM)和Scale-wise Feature Aggregation Module (SFAM)。其中,TUMs和FFMs提取出更具代表性的多级多尺度特征。
(2) Depth image pixels corresponding to the object are projected to generate the object's frustum point cloud, and a multi-modal feature fusion strategy simplifies the object's frustum point cloud, so as to remove outlier points and reduce the number of point clouds. This can replace the 3D...
used to obtain the local detail and global context features. Third, the N-CAM and MS-FPN are used to obtain the foreground objects' semantic feature and position feature, and suppress the background region noise interference. Finally, we use the FA module to enhance the category and feature ...
In this paper, we present a Multi-level Feature Enhancement Network (MFENet) to enhance the feature extraction of each level in backbone. This approach can achieve high performance while maintaining high inference speed. We first rely on a Spatial and Edge Extraction Module with the Laplace ...
With the help of a stand-alone module to estimate the disparity and compute the 3D point cloud, we introduce the multi-level fusion scheme. First, we encode the disparity information with a front view feature representation and fuse it with the RGB image to enhance the input. Second, ...
In addition, the model focuses on learning both high-level and low-level feature information. Zhang et al.34provided a coarse-to-fine CD model, which can effectively solve the problem of excessive weight of foreground information prediction through two-stage feature fusion. Some CD models10,17,...
To fully interact with the feature encoding extracted from different paths, we propose a novel multi-feature fusion module (MFF) that can tightly link the feature Experimental validation In this section, we first introduce the dataset and describe the implementation details and the experimental setup...
如图所示,MML包含一个特征提取器(feature extractor)、一个多层次度量模块(multi-level metric module)和一个融合层(fusion layer)。 Feature Embedding with Local Representations。给定一副输入图像,特征提取器输出一个三维特征向量 F (3D,C×H×W),它既可以被视作HW C-dimensional pixel-level feature descriptors...
所以提出M2Det模型,主要是Multi-Level Feature Pyramid Network(MLFPN)模块,其由Thinned U-shape Modules(TUM),Feature Fusion Modules(FFM)和Scale-wise Feature Aggregation Module(SFAM)组成,可以看出本文的工作量肯定不小。 2 相关模型 如下图所示,文中列举了四种风格的特征金字塔:SSD型、FPN型、STDN型,以及本...