We design the Multi-Scale Feature Aggregation Module (MSFA), which directly aggregates the change features of different layers obtained by the LFFM and adaptively predicts a set of weights according to the different importance of the features of each layer, which avoids the loss of some informati...
【3D目标检测】Voxel-FPN: multi-scale voxel feature aggregation in 3D object detection from point cloud解读 神马嘴脸 3 人赞同了该文章 前言 1. 为什么要做这个研究?目前基于体素的3D目标检测多为从单一尺度的体素中提取体素特征信息,再进行检测,而作者提出了一种密集聚合结构,以自底向上的方式从多尺度编码体...
DDocE: Deep Document Enhancement with Multi-scale Feature Aggregation and Pixel-Wise Adjustments 来自 Springer 喜欢 0 阅读量: 45 作者:KOM Bogdan,GAS Megeto,R Leal,G Souza,LN Kirsten 摘要: Digitizing a document with a smartphone might be a difficult task when there are suboptimal environment ...
2.多尺度信息聚合 Multi-scale context aggregation A context module is constructed based on the dilated convolution as below: The Basic Context Module and The Large Context Module 这个上下文模块一共有8层,其中7层运用了具有不同膨胀因子的3×3卷积(膨胀因子分别为:1, 1, 2, 4, 8, 16, 1) 最后一...
MLFPN主要包含三个模块,特征融合模块FFM,细化U形模块,基于尺度的特征聚合模块(Scale-wise Feature Aggregation Module),FFMv1通过将骨架网络提取的特征融合来增强语义信息,每个TUM生成一组多尺度特征,然后将TUM和FFMv2交替组合来提取多尺度特征。另外,SFAM通过基于尺度的特征连接(concatenation)和自适应注意力机制将不同...
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 Aggregation Module)将这些多层级多尺度特征依据相同尺度进行整合得到最终的特征金字塔。基于MLFPN的M2Det是一个高效的end-to-...
Multiscale feature fusion module, MFFM In deep neural networks, as the network depth increases, while more advanced semantic information from the image is extracted, the decline in resolution also results in the gradual loss of intricate detail within the image. Due to the trade-off between high...
c. Scale-wise Feature Aggregation Module: We get the multi-level multi-scale feature, and try to re-allocate a weight for them to force the feature focusing more on the most useful channels/levels. Depending on the compress ratios, we use a SE attention module for each scale feature to ...
MULTI-SCALE CONTEXT AGGREGATION BY DILATED CONVOLUTIONS这篇论文是ICLR 2016会议文章,这里简短记录下论文的主要内容。时间精力有限,只是粗读了下论文的网络结构,难免有纰漏。 论文应该借鉴了Deeplab提出的带…
Feature aggregation can make full use of the semantic information of high-level features and the fine-grained features of low-level features, integrate the information of different levels, and enhance the feature expression ability of the network. At the same time, the pooling module and 1 ×...