This paper proposes a lightweight multi-scale feature pyramid structure, which extracts features from network layers of different scales and aggregates them to supplement spatial detail information. Meanwhile, this paper adopts a pair of complementary attention modules, which pay attention to the ...
两种进一步创新的结构:KP-Pyramid 、RandLA-Pyramid 这一个模块就是为了掩饰提出的金字塔结构的encoder-decoder架构是通用的,故将其用在了另外两个网络中,并达到了好的效果。 Conclusion 论文提出了一种三向金字塔架构来处理和融合多尺度信息以进行点云分割。 论文使用了几个简单但有效的组件改进了常用的encoder-decoder...
Title: PSConv: Squeezing Feature Pyramid into One Compact Poly-Scale Convolutional Layer 作者:Duo Li, Anbang Yao, and Qifeng Chen 发表单位:The Hong Kong University of Science and Technology;Intel Labs China 发表于:ECCV 2020 关键词:卷积核,多尺度 一句话总结:在卷积核内部设计多尺度信息提取。对...
In light of the shortcomings of the feature pyramid network (FPN) in preserving semantic information during adoption and its limited model receptive field, we present a novel approach for improving object detection accuracy in the form of a deep attention-guided multi-scale feature pyramid network ...
Multi-scale text detectionGrouped pyramid moduleEfficient and effectiveScene text detection has attracted many researches due to its importance to various applications. However, current approaches could not keep a good balance between accuracy and speed, i.e., a high-performance accuracy but with a ...
this study proposes a pyramid-attention based multi-scale feature fusion network (PAMF-Net) that combines the pyramid attention mechanism and feature aggregation. Initially, the MS and PAN images are input to the network, and the PAN images pass through the input pyramid branch to generate a mul...
Presently, research on deep learning-based change detection (CD) methods has become a hot topic. In particular, feature pyramid networks (FPNs) are widely used in CD tasks to gradually fuse semantic features. However, existing FPN-based CD methods do not correctly detect the complete change reg...
Efficient Pyramid Multi-Scale Channel Attention Modules: To capture the fine-grained multi-scale local feature and establish the long-term dependencies between channels, an efficient pyramid-type multi-scale channel attention (EPMCA) module is proposed, as shown in Fig. 5. It first extracts the ...
浅层可以在高空间分辨率下用小的channel维度建立简单的low-level feature,而深层则可以用更大的channel维度建立更high-level的语义信息,这个是特征金字塔的思想。 Multi Head Pooling Attention:相比于MHA加入了pooling操作,主要作用是改变token个数。其中cls token没有参与pooling操作。
SSD [29] and MSCNN [2] predict objects at multiple layers of the network without merging features. Feature pyramid networks [26] extend the backbone model with a top-down pathway that gradually recovers feature resolution from 1/32 to 1/4, using bilinear upsampling and lateral connection. The...