Attention mechanismAt present, occlusion and similar appearance pose serious challenges to the task of person re-identification. In this work, we propose an efficient multi-scale channel attention network (EMCA) to learn robust and more discriminative features to solve these problems. Specifically, we...
本文共分为五个部分:引言、efficient multi-scale attention module概述、解释说明efficient multi-scale attention module的关键要点、其他相关研究工作概述和比较分析以及结论。通过这样的结构,读者能够全面了解并深入探索efficient multi-scale attention module的概念和其在计算机视觉领域中的重要性。 1.3 目的 本文旨在向读...
Efficient ViT Variants 现在efficient ViT的为了降低计算量,设计思路主要分为两类,一个是使用local self-attention,如Swin Transformer,一个是把tokens merge起来减小token数量,如PVT。 以往的工作对于同一个layer内只有一个scale,而忽视了大小object的不同。 本文提出的方法可以动态地同一层保留不同scale的feature,自适...
MSCA: Multi-Scale Channel Attention Module. Contribute to eslambakr/EMCA development by creating an account on GitHub.
CA-MCNN is a multi-scale convolutional neural network with the combination of pooling layers, efficient channel attention block and parallel feature fusion mechanism. We use the bearing dataset to find suitable pooling parameters and mini-batch size for the model, and verify the effectiveness of CA...
This study proposes a lightweight multi-scale CNN with an efficient channel attention mechanism for extracting deep features from raw multi-channel electromyography data. The network architecture is inspired by MobileNetV2 [27] and InceptionTime [28]. The lower limb gait recognition method is illustrat...
multi-scale fine fusion: 在这个除段引入 了 channel attention unit 来实现 discriminative learning enahncement through focusing on the most informative scale-specific knowledge, making the cooperative representation more efficient. 为了降低复杂度,采用了 U-shape 的结构,如下图所示。 reain streak reconstruc...
Wang, Q.et al.Eca-net: Efficient channel attention for deep convolutional neural networks. InProceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition11534–11542 (2020). Hu, J., Shen, L. & Sun, G. Squeeze-and-excitation networks. InProceedings of the IEEE Conference ...
Consequently, we propose a novel architecture called Attention based multi-scale nested network (AMNNet), specifically designed for efficient biomedical image segmentation. AMNNet comprises four components: early ReSidual U-CBAM (RSUC) modules and convolutional stages, a MLP stage in latent stage, ...
Token-aware Transformer模块在生成key和value 时插入尺度缩减模块以减小高分辨率特征图的形状。该操作降低了计算复杂度并提高了注意力机制的效率。而Channel-aware Transformer可以进一步降低计算复杂度,同时可以获得通道间的依赖关系。 解码器包括三个efficient transformer块和四个Patch Expanding块,以恢复与输入相同分辨率的...