Convolutional Block Attention Modules
PulseAugur coverage of Convolutional Block Attention Modules — every cluster mentioning Convolutional Block Attention Modules across labs, papers, and developer communities, ranked by signal.
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Deep learning enhances 4D flow MRI for better blood flow assessment
Researchers have developed a deep learning framework to improve the resolution and reduce noise in 4D flow MRI data, a technique used for visualizing blood flow. The proposed model integrates multi-scale feature extract…
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MSCA-UNet enhances image segmentation with multi-scale context and attention
Researchers have developed MSCA-UNet, an enhanced U-Net architecture for image segmentation that improves upon the baseline model's performance. By incorporating multi-scale contextual aggregation at the bottleneck and …
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ForensicNet: Lightweight AI Model Enhances Face Identification Accuracy
Researchers have developed ForensicNet, a lightweight deep learning model designed for automated face identification in forensic settings. This model integrates the MobileNetV2 architecture with Convolutional Block Atte…
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New AI framework enhances chest X-ray classification with explainability
Researchers have developed PulmoSight-XAI, a novel framework for classifying chest X-rays that addresses challenges like class imbalance and feature loss. The system utilizes a multi-view attention ensemble with gradien…
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New AG-EfficientNet improves criminal identification from surveillance images
Researchers have developed a new framework called AG-EfficientNet to improve criminal identification from surveillance images. This model integrates EfficientNet-B0 with Convolutional Block Attention Modules (CBAM) to b…