3. 解释说明 efficient multi-scale attention module 的关键要点: 3.1 多尺度特征提取和整合策略: 多尺度特征提取是指在图像或视频处理中,通过使用不同感受野大小的卷积核进行多层级的特征提取。在efficient multi-scale attention module中,采用了一种创新的策略来同时提取不同尺度下的特征。具体而言,模块中包含多个并...
1研究动机 这篇论文提出了一种新型的高效多尺度注意力(Efficient Multi-Scale Attention, EMA)模块,旨在解决现有注意力机制在提取深度视觉表示时可能带来的计算开销问题。作者指出,尽管通道或空间注意力机制在多种计算机视觉任务中表现出显著的有效性,但通过通道降维来建模跨通道关系可能会影响特征的深度表示。因此,EMA模...
multi-scale attention modulereal-timeAs one of the key technologies of intelligent vehicles, traffic sign detection is still a challenging task because of the tiny size of its target object. To address the challenge, we present a novel detection network improved from yolo-v3 for the tiny ...
EMA(Efficient Multi-Scale Attention)模块是一种新颖的高效多尺度注意力机制,旨在提高计算机视觉任务中的特征表示效果。 EMA注意力模块通过结合通道和空间信息、采用多尺度并行子网络结构以及优化坐标注意力机制,实现了更加高效和有效的特征表示,为计算机视觉任务的性能提升提供了重要的技术支持。 通道和空间注意力的结合:E...
Implementation Code for the ICCASSP 2023 paper " Efficient Multi-Scale Attention Module with Cross-Spatial Learning" and is available at: https://arxiv.org/abs/2305.13563v2 - YOLOonMe/EMA-attention-module
classEMA(nn.Module):def__init__(self, channels, c2=None, factor=32):super(EMA, self).__init__() self.groups = factor# 分组数,默认为32assertchannels // self.groups >0# 确保通道数能够被分组数整除self.softmax = nn.Softmax(-1)# 定义 Softmax 层,用于最后一维度的归一化self.agp = ...
MSCA: Multi-Scale Channel Attention Module. Contribute to eslambakr/EMCA development by creating an account on GitHub.
Inspired by the attention mechanism, a multi-task learning framework for face-based age estimation called multi-task multi-scale attention is proposed. First, the authors embed the alternative strategy structure of dilated convolution into ResNet34 to construct a multi-scale attention module (MSA) ...
Architecturally, it consists of a Multi-Scale Coupled Channel Attention (MSCCA) module, and a Multi-Scale Coupled Spatial Attention (MSCSA) module. Specifically, the MSCCA module is developed to achieve the goal of self-attention learning linearly on the multi-scale channels. In parallel, the ...
A multimodal attention-fusion convolutional neural network for automatic detection of sleep disorders First, multiple convolutional neural network branches are used to extract time-invariant features of multimodal signals. Then, a multi-scale attention module ... W Wang,J Li,Y Fang,... - 《Applied...