Moreover, the proposed CMSFL module requires significantly fewer trainable parameters and FLOPs that helps train the model efficiently, address overfitting, and achieve better generalizability to test data. The number of trainable parameters and FLOPs in an\(l^{th}\)convolutional layer can be compute...
First, methods involving the use of traditional convolutional neural networks mainly focus on local perception domains; they lack global perception and do not assign different weights to different parts of the input. Although an attention mechanism places more emphasis on the global perception domain, ...
carotid artery with plaque (Fig.3A). Collected image serve as the foundation for the whole reconstruction module. These images have been annotated and preprocessed (Fig.3B). The convolutional neural networks (CNNs) are trained using these pairs of original and annotated images. The trained models...
A novel multiscale dilated convolutional network (MSDC-Net) is proposed against the scale difference of lesions and the low contrast between lesions and normal tissues in CT images. In our MSDC-Net, we propose a multiscale feature capture block (MSFCB) to effectively capture multiscale features...
First, we design the Coordinate Reconstruction Attention Mechanism (CRAM), which enhances the capture of impulse information by coordinate reconstruction. In addition, a multiscale convolutional token embedding module is constructed to extract local features at different scales, and its ability for ...
A spatial-temporal attention-based method and a new dataset for remote sensing image change detection Remote Sens. (2020) Chen, H., Wu, C., Du, B., Zhang, L., 2019a. Deep Siamese multi-scale convolutional network for change detection in... ChenH. et al. Change detection in multisour...
SCA-CNN: Spatial and Channel-wise Attention in Convolutional Networks for Image Captioning Paper Reading -- Octave Convolution 【paper reading】Learning Shape Priors for Single-View 3D Completion and Reconstruction Paper | Drop an Octave: Reducing Spatial Redundancy in Convolutional Neural Networks with ...
A feature aggregation module is sequentially designed with dual attention blocks, yielding multi-scale feature maps effectively. We aggregate the multi-scale maps by a concatenation operation. Finally, a simple convolutional layer is adopted to generate residual images. With the residual learning ...
(SE) block to rescale the different channels to build interdependencies between channels. Convolutional Block Attention Module (CBAM)44proposes an efficient module to exploit both spatial and channel attention, which improves the performance compared to SENet. Non-Local Networks (NLNet)45introduces a ...
regions, Woo et al.45proposed the Convolutional Block Attention Module (CBAM). This module combines channel attention and spatial attention in a stacked manner, decoupling channel attention maps and spatial attention maps to improve computational efficiency. It also leverages global pooling to ...