Convolutional Block Attention Module
PulseAugur coverage of Convolutional Block Attention Module — every cluster mentioning Convolutional Block Attention Module across labs, papers, and developer communities, ranked by signal.
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New pipeline enhances tiny object detection in aerial images
Researchers have developed strategies to improve the detection of tiny objects in aerial images, a task that challenges standard object detection models like YOLOv8. Their approach involves enhancing input resolution, e…
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AI model accurately classifies peach leaf damage with attention mechanisms
Researchers have developed a new deep learning model for classifying peach leaf damage, achieving high accuracy on a benchmark dataset. The model, an enhanced EfficientNetB5 incorporating a Convolutional Block Attention…
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New audit protocol assesses AI explanation faithfulness in visual inspection
Researchers have developed a new method for auditing the explanations generated by deep learning models used in industrial visual inspection. This "architecture-aware" protocol assesses how faithfully an explanation met…
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AI model classifies wildfire smoke density with uncertainty estimates
Researchers have developed a new deep learning framework to classify wildfire smoke density from satellite imagery, categorizing it into light, moderate, and heavy severity. This model provides decomposed epistemic and …
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New WiFi fall detection system uses AI to adapt to unseen environments
Researchers have developed a novel framework for device-free fall detection using WiFi Channel State Information (CSI). The system employs an Attention-Enhanced CNN-Transformer hybrid architecture to overcome performanc…
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New network SANet improves infrared small target detection with attention
Researchers have developed SANet, a novel Selective Attention-based Network designed to improve the detection of small, dim targets in infrared imagery. This network addresses limitations in existing encoder-decoder arc…
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Researchers enhance CNNs with CBAM for improved multi-label X-ray diagnosis
Researchers have developed a new strategy to improve the accuracy of deep learning models in diagnosing multiple conditions from chest X-rays. Their method integrates the Convolutional Block Attention Module (CBAM) with…
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AI model efficiently detects bridge cracks from UAV imagery
Researchers have developed a lightweight convolutional neural network framework designed for real-time crack classification in UAV bridge inspections. The system addresses challenges like weak crack features, poor imagi…
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Enhanced YOLOv8n model boosts real-time vehicle detection with attention and efficient convolution
Researchers have developed an improved YOLOv8n model for real-time vehicle detection, incorporating Ghost Modules, CBAM, and DCNv2. This enhanced model aims to boost performance in intelligent transportation systems by …