MSDA(Multi-Scale Dilated Attention)的工作原理如下: 特征映射处理:给定一个特征映射X,通过线性投影得到相应的查询(Q)、键(K)和值(V)。 多头设计:将特征映射的通道分成n个不同的头部,每个头部使用不同的扩张率进行多尺度的Sliding Window Dilated Attention(SWDA)操作。 多尺度SWDA操作:每个头部的SWDA操作用于在不...
mechanisms, and designs multi-scale dilated convolution and multi-scale feature fusion modules to enhance water body extraction performance in complex scenarios. Specifically, in the proposed model, improved residual connections are introduced to enable the learning of more complex features; the attention ...
Melanoma segmentation based on a convolutional neural network (CNN) has recently attracted extensive attention. However, the features captured by CNN are always local that result in discontinuous feature extraction. To solve this problem, we propose a novel multiscale feature fusion network (MSFA-Net...
The extracted features by multi-scale CNN and bi-LSTM are contacted into a vector. And the feature vector is fed into the arbitration network for their weights redistribution. The arbitration network depends mainly on multi-head attention mechanism to efficiently extract information from the input fe...
that combine dilated convolution and multiscale fusion. in multi-organ segmentation tasks, multiscale feature fusion is widely used because of the different sizes of organs. for example, jia and wei [ 80 ] introduced the feature pyramid into a multi-organ segmentation network using two opposite ...
3.1.1 Multiscale strategy To achieve pixel-level accuracy, dilated convolutions (Yu & Koltun, 2016), a.k.a., atrous convolution, are often used, in which the elements at noncontiguous positions in a kernel are integrated to increase the amount of spatial context. Zhao and Du (2016) propo...
A top-down horizontal connection structure is used for multi-scale fusion. (e) MRFENet. Our proposed MRFENet uses the dilated bottleneck as the base unit to expand the receptive field and obtains features that facilitate small object detection. In this figure, the detection head network is ...
connection network structure. In addition, some methods5,27,36use attention modules to emphasize the response of foreground regions and calibration channels to make the network more adaptable. These methods have proved that multi-scale information and attention mechanism are effective for segmentation ...
As the CNN model is not invariant to rotation and scale, it is a tremendous task to segment an object that can be moved in the image. One of the key concerns about using a CNN model in the field of medical imaging lies in the time of the evaluation, as many medical applications need...
Indoor point cloud Object detection Multi-head attention mechanism Deep multi-scale contextual feature Deep learning 1. Introduction The efficient and accurate detection of indoor objects based on 3D point cloud has become very important for the success of various indoor applications, including real-time...