input_X, out_dim, reduction_ratio=16, layer_name='SE-block'): """Squeeze-and-Excitation (SE) Block SE block to perform feature recalibration - a mechanism that allows the network to perform feature recalibration, through which it can learn to use global information to selectively emphasise ...
To reduce parameter overhead, the hidden activation size is set to $\mathbb{R}^{C/r×1×1}$, where $r$ is the reduction ratio. After the shared network is applied to each descriptor, we merge the output feature vectors using element-wise summation. In short, the channel attention is ...
Specially, the proposed cycleSimulationGAN constrains the structural similarity between the synthetic and input images for better structural retention characteristics. Additionally, we propose to equip a novel channel-wise attention mechanism based on the traditional generator of GAN to enhance the feature ...
In the ghost module, , we can calculate the ratio of the parameters and operations of the two convolutions: (13) (14)Therefore, the parameters and computational complexity of the ghost module are just of general standard convolution. In the object detection neural network, the feature map ...
After obtaining the attention vector of all C channels, each channel of input X is scaled by the corresponding attention value: \widetilde {X}_{:,i,:,:} = att_i X_{:,i,:,:}, \:\:\: s.t. \;\; i \in \{0,1,\cdots ,C-1\}, (5) in which X is the output of ...
1. At the first stage, a CNN is applied to extract feature maps from the input colonoscopy images and subsequently develop a region proposal network (RPN) which suggests bounding boxes of candidate objects on the feature maps. At the second stage, the RoIAlign (Region of Interest Align) ...
Multi-scale feature input is used to retain more detailed features of different dimensions. A lightweight convolutional neural network is constructed. Finally, a transfer learning method is applied to freeze lower structures of the network and fine-tune higher structures of the model using small ...
input to the transformers and subtly fused them after feature extraction. Some methods improved the classification performance by adding operations, e.g., multi-scale, spatial attention, and feature aggregation. However, our CSAT network did not include these advanced skills. In the future, we ...
(a) Input MS image. (b) First feature map. (c) Second feature map. (d) Third feature map. 3.3. Attention Feature Fusion Module The feature maps of SAR and MS images are obtained after the spatial feature extraction branch and the spectral retention branch. In order to fully utilize ...
where x represents the input samples of the RDCU. The 𝑋𝑙Xl represents the the input feature map for the immediate succeeding sub-sampling or up-sampling layers in the encoding and decoding convolutional units of the proposed method. Figure 3. The submodule of RDCU. The DCnov in the fig...