To address this concern, we introduce EMCAD, a new efficient multi-scale convolutional attention decoder, designed to optimize both performance and computational efficiency. EMCAD leverages a unique multi-scale
[2], where a 3D network architecture with two convolutional pathways was presented for the efficient multi-scale processing of multi-modal MRI volumes. If the use of the software positively influences your endeavours, please cite [1].
It was employed for our research presented in [1],[2], where a 3D network architecture with two convolutional pathways was presented for the efficient multi-scale processing of multi-modal MRI volumes. If the use of the software positively influences your endeavours, please cite [1]. [1] ...
Multi-scale attention guided pose transfer 2023, Pattern Recognition Citation Excerpt : In [21], the authors introduce cascaded mutually activated residual linear modeling (MARLM) modules to learn progressive latent transformation. In [22], the authors overcome the limitations of convolutional neural ne...
First, a weight-based feature fusion block is designed to adaptively fuse information from several multi-scale feature maps. The feature fusion block can exploit contextual information for feature maps with large resolutions. Then, a context attention block is applied to reinforce the local region ...
Multi-scale convolution networks for seismic event classification with windowed self-attention Yongming Huang Yi Xie Guobao Zhang Journal of Seismology(2025) Processing of electrical resistivity tomography data using convolutional neural network in ERT-NET architectures ...
Zhang, H., Zu, K., Lu, J., et al.: Epsanet: An efficient pyramid squeeze attention block on convolutional neural network. In: Proceedings of the Asian Conference on Computer Vision, pp. 1161–1177. https://doi.org/10.48550/arXiv.2105.14447 (2022) Zhou, B., Duan, X., Ye, D., ...
Transformer utilizes the self-attention mechanism to model the global relationships among positions in a sequence [15]. Compared to traditional convolutional neural networks, Transformer has a natural advantage in capturing global contextual information. The emergence of Vision Transformer (ViT) extends the...
Transforming image denoising models that are initially designed for grayscale images to efficiently process color images is a complex endeavor. Such models, especially those employing advanced techniques like Progressive Residual and Convolutional Attention Feature Fusion, often struggle to accurately capture ...
To better handle these challenges, the paper proposes a novel framework, multi-scale, deep inception convolutional neural network (MDCN), which focuses on wider and broader object regions by activating feature maps produced in the deep part of the network. Instead of incepting inner layers in ...