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 ...
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
AlexNet The first architecture designed for image classification that used successive convolutional layers was AlexNet. It contains eight layers (5 convolutional layers and three fully connected layers) [66]. It won the ImageNet Large Scale Visual Recognition Challenge (ILSVRC) 2012. AlexNet achieved a...
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...
In federated learning, the heterogeneity of statistical data is a crucial research issue. FedAvg is one of the pioneering works to address this issue, using weighted averaging of local weights based on local training scale and has been widely recognized as a baseline for federated learning [60]....
Multi-scale depthwise temporal convolution (MDTC) feature extractor. We first propose a multi-scale depthwise temporal convolution (MDTC) feature extractor, where stacked depthwise one-dimensional (1-D) convolution with dilated convolution is adopted to efficiently model long-range dependency of speech...
which adjusts the size of convolutional kernels through a dynamic selection mechanism to adapt to the multi-scale characteristics of the input. SKNet aggregates information from multiple kernels to achieve adaptive adjustment of the neuron’s receptive fields. The SK convolution comprises three operation...
Fig. 7: Predicting of BSRs on a seismic data section using a simple sliding window method. The dotted rectangles are illustrations of slice windows moving row by row on specific seismic section, while the solid squares denote the windows with real scale on the section. The prediction results ...
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...