global context attention based convolutional neural network Search Results


convolutional neural network-based pavement crack segmentation using pyramid attention network  (IEEE Access)
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IEEE Access convolutional neural network-based pavement crack segmentation using pyramid attention network
Convolutional Neural Network Based Pavement Crack Segmentation Using Pyramid Attention Network, supplied by IEEE Access, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
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convolutional neural network-based pavement crack segmentation using pyramid attention network - by Bioz Stars, 2026-03
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efficient global attention block (gab) for feed-forward convolutional neural networks (cnn)  (Mendeley Ltd)
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Mendeley Ltd efficient global attention block (gab) for feed-forward convolutional neural networks (cnn)
Comparison of existing methodologies.
Efficient Global Attention Block (Gab) For Feed Forward Convolutional Neural Networks (Cnn), supplied by Mendeley Ltd, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
https://www.bioz.com/result/efficient global attention block (gab) for feed-forward convolutional neural networks (cnn)/product/Mendeley Ltd
Average 90 stars, based on 1 article reviews
efficient global attention block (gab) for feed-forward convolutional neural networks (cnn) - by Bioz Stars, 2026-03
90/100 stars
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Comparison of existing methodologies.

Journal: Scientific Reports

Article Title: Reinforcement-based leveraging transfer learning for multiclass optical coherence tomography images classification

doi: 10.1038/s41598-025-89831-2

Figure Lengend Snippet: Comparison of existing methodologies.

Article Snippet: , Efficient global attention block (GAB) for feed-forward convolutional neural networks (CNN) , Mendeley Data, 2018 , Accuracy, Precision, Recall, F1-Score, and Confusion Matrix , 97% Accuracy.

Techniques: Comparison, Biomarker Discovery, Labeling, Extraction, Standard Deviation, Blocking Assay