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 depth-wise convolution block, significantly enhancing feature maps through multi-scale convolution...
Paper tables with annotated results for EMCAD: Efficient Multi-scale Convolutional Attention Decoding for Medical Image Segmentation
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] ...
Efficient Multi-Scale 3D Convolutional Neural Network for Brain Lesion Segmentation - fuchaosh/deepmedic
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., ...
DsP-YOLO: An anchor-free network with DsPAN for small object detection of multiscale defects 2024, Expert Systems with Applications Show abstract Defect detection of the surface of wind turbine blades combining attention mechanism 2024, Advanced Engineering Informatics Show abstract A small sample nonst...
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 ...
(2)在上述分析的基础上,我们尝试开发一种用于深度cnn的极轻量级通道注意模块,提出了一种高效通道注意(Efficient channel attention, ECA)模型,该模型的复杂性几乎没有增加,但有明显的改进。 (3)在ImageNet-1K和MS COCO上的实验结果表明,该方法具有较低的模型复杂度,同时具有较好的性能。
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[6] proposed HM-YOLOv5 network for detecting defects of hot-pressed LGPs. They reduced information loss and improved the defect detection ability of the network by constructing a hybrid attention module (HAM) and a pyramid-structured multi-unfolding convolutional module (MCM), achieving an mAP ...