简介:【YOLOv11改进 - 注意力机制】 MSDA(Multi-Scale Dilated Attention):多尺度空洞注意力本文介绍了一种高效的视觉变换器——DilateFormer,通过多尺度扩张注意力(MSDA)模块,在保持高性能的同时显著降低计算成本。MSDA通过在滑动窗口内模拟局部和稀疏的块交互,实现了多尺度特征聚合。实验结果显示,DilateFormer在ImageN...
In particular, we design a multi-scale dilated attention mechanism, which captures long-range dependencies through local and sparse block interactions from visual data. Additionally, SS-GANs based on assisted rotational loss is implemented to utilizes users' historical interaction data to predict events...
DilateFormer 的关键设计概念是利用多尺度空洞注意力(Multi-Scale Dilated Attention, MSDA)来有效捕捉多尺度的语义信息,并减少自注意力机制的冗余。 如上图所示,DilateFormer的整体架构主要由四个阶段构成。在第一阶段和第二阶段,使用 MSDA,而在后两个阶段,使用普通的多头自注意力(MHSA)。对于图像输入,DilateFormer ...
Aiming at the detection problem of irregular multi-scale insect pests in the field, a dilated multi-scale attention U-Net (DMSAU-Net) model is constructed for crop insect pest detection. In its encoder, dilated Inception is designed to replace the convolution layer in U-Net to extract the ...
【ARIXV2209】Multi-Scale Attention Network for Single Image Super-Resolution代码:github.com/icandle/MAN 这是来自南开大学的工作,将多尺度机制与大核注意机制结合,用于图像超分辨率。 2022年初,大核卷积火了,Visual Attention Network (VAN)提出将大核卷积划为:depth-wise conv,dilated conv,和 point-wise con...
To address these limitations, a novel approach is proposed that integrates a multi-scale attention block with dilated convolution into the neural network architecture within the Deep Image Prior framework. This innovative solution aims to enhance the network's ability to capture fine details while ...
1、Multi-scale Large Kernel Attention (MLKA) MLKA首先使用 Point-wise conv 改变通道数,然后将特征 split 成三组,每个组都使用 VAN 里提出的大核卷积来处理(即depth-wise conv,dilated conv,和 point-wise conv 的组合)。三组分别使用不同尺寸的大核卷积(7×7、21×21、35×35),膨胀率分别设置为(2,...
We propose a novel network named Multi-scale Attention-Net with the dual attention mechanism to enhance the ability of feature representation for liver and tumors segmentation 我们提出了一种新的具有双重注意机制的多尺度注意网络,以增强肝脏和肿瘤分割的特征表示能力。
attention block (MWAB). The MSCB utilizes three parallel convolutions with different dilation rates, which are connected hierarchically. The unique connection can effectively extract multi-scale and multi-level features while expanding the receptive field. Particularly, the dilated convolution replaces ...
FPN has made significant contributions to one-stage anchor-free object detection. From an optimization perspective, YOLOF introduces an alternative solution regardless of complex feature pyramids. In this framework, two central components are specified, namely, dilated encoder and uniform matching, which ...