In order to solve these problems, a multi-dimensional attention feature aggregation stereo matching algorithm was proposed. The two-dimensional (2D) attention residual module is designed by introducing the adaptive 2D attention residual unit without dimensionality reduction into the original residual ...
They demonstrate the benefit of using a two-dimensional network architecture to balance the trade-off between efficiency and accuracy [31]. In a study that also addresses the deep learning-based segmentation of structures in newborn brains, the authors also have to deal with the unique attributes...
-dimensional HR image vectors used for the training. The paired set of LR training image vectors is denoted by SLR={I→LR,n∈RMLNLC×1 n=1,2,⋯,T}. As depicted in Fig. 2, paired HR and LR images act as parallel inputs to the Master and Follower AEs, respectively....
d represents the dimensional of Qs or Ks. At last, the result values are concatenated and projected once again, resulting in the arbitration values by Eq. (8), $$\begin{aligned} arbitration=FC(Concat(head_1, \cdots , head_N)). \end{aligned}$$ (8) Figure 5 Framework of multi-...
In CNNs, shallow convolutional layers usually extract low-dimensional features, while high-dimensional features are usually obtained by deep convolutional layers. However, a problem exists, i.e., although rich semantic information can be extracted from high-level features, the target location informatio...
[23] used the pretrained CNN model to extract the high-dimensional features of medical images, and combined them with general GIST feature and bag-of-visual words (BoVW) features as the input of support vector machine (SVM) to detect thoracic lesions. Wanget al. [14] developed a Ddep ...
to extract high-dimensional feature information. The Position-wise Attention Block is used to capture the spatial dependencies of feature maps, and the Multi-scale Fusion Attention Block is to aggregate the channel dependencies between any feature maps via fusing High and Low-level feature information...
Methods represented by recurrent neural networks (RNNs) extract temporal features from one-dimensional PQD signals and then perform classification25. Literature26 proposes introducing a dual attention mechanism in Bi-LSTM to increase the weight of important features, reducing computational complexity and ...
Compared with SegNet network, U-Net introduced the skip connections between mirror layers which could increase information flow and integrate detailed features with the high resolution and high-dimensional features from the backbone network. As a result, in this work, U-Net is chosen as the ...
f1D1, f1D3 and f1D5 denote the one-dimensional convolution operation with kernel sizes of 1, 3, and 5, respectively. The funciton Means is the average of the three-channel descriptors in the spatial dimension, and the dimension of the output of Means is R1×1×C. σ represents the ...