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.
6 day(s) with sentiment data
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Blasto-Net: AI model for blastocyst analysis in IVF · 2 sources tracked
Researchers have developed Blasto-Net, a novel multi-task deep learning model designed for comprehensive blastocyst analysis in in vitro fertilization (IVF). This model simultaneously performs segmentation of key compar…
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Deep learning framework enhances sperm morphology classification with improved interpretability
Researchers have developed an attention-guided deep learning framework to improve the interpretability and accuracy of sperm morphology classification. By integrating a pre-trained EfficientNet-B0 model with a Convoluti…
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AI models achieve top ranks in ICRA 2026 GOOSE 2D segmentation challenge · 4 sources tracked
Researchers have developed advanced methods for the ICRA 2026 GOOSE 2D Fine-Grained Semantic Segmentation Challenge, achieving top rankings. One team leveraged the Segment Anything Model 3 (SAM3) with a self-distillatio…
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New architecture unifies materials ontologies for regulatory compliance
Researchers have proposed a novel multi-level architecture for reusable materials ontologies, addressing fragmentation in the field. This architecture features independent classification axes for abstraction level and c…
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New framework enhances AI for noisy marine bioacoustic monitoring
Researchers have developed GetNetUPAM, a novel nested cross-validation framework designed to improve the reliability of marine bioacoustic monitoring systems. This framework addresses issues of high noise and low 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 …