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
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 ...
Therefore, this paper proposes a new bridge crack segmentation method based on parallel attention mechanism and multi-scale features fusion on top of the DeeplabV3+ network framework. First, the improved lightweight MobileNet-v2 network and dilated separable convolution are integrated into the original...
Brain tumor segmentation using multi-scale attention U-Net with EfficientNetB4 encoder for enhanced MRI analysis Article Open access 22 March 2025 Enhancing brain tumor segmentation in MRI images using the IC-net algorithm framework Article Open access 08 July 2024 Brain tumor image segmentation...
An efficient multi-scale fusion network for 3D organ at Risk (OAR) Segmentation. 2022; https://arxiv.org/abs/2208.07417. Accessed 3 Oct 2022. Oktay O, Schlemper J, Folgoc LL, Lee M, Heinrich M, Misawa K, et al. Attention U-net: learning where to look for the pancreas. arXiv; ...
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...
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...
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 ...
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 ...
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 ...