Tag: Attention Mechanism

Researchers have developed S2Fusion, a local-to-global framework that improves infrared and visible image fusion through cross-modal dynamic kernels and a ...

Korean researchers have built a hybrid Vision Transformer and CNN generative model that enhances ultrasound image quality with state-of-the-art fidelity ...

An Indian research team has developed GraphX, an explainable graph transformer framework that classifies nail diseases with 94.97 percent accuracy ...

A new mask-aware transformer architecture that explicitly tracks missing macroeconomic data outperformed statistical, recurrent, and modern forecasting benchmarks in fixed-origin ...

A new multi-dimensional evaluation framework shows that the most accurate intrusion detection model is often not the best choice for ...

A new hybrid deep learning architecture combining a fine-tuned U-Net with a time-weighted multi-task bi-directional LSTM significantly improves real-time detection ...

A new recommendation framework called BLMRec models the temporal validity of browsing, favoriting, and add-to-cart behaviors to improve purchase ranking ...

Researchers have developed PeTMiner, a deep learning framework that uses a plant RNA foundation model, hierarchical attention, and adaptive loss ...

A Transformer deep learning model predicts cancer cell fates directly from live-cell videos with high accuracy up to eight hours ...

Researchers have developed a two-stage attention-enhanced deep learning framework that segments breast lesions in ultrasound images before classifying them, achieving ...

Researchers in China have improved facial expression recognition by redesigning how a ConvNeXt network downsamples images and integrates spatial-channel attention, ...

Researchers have developed a graph-based deep learning network that decodes imagined movements from EEG signals with accuracies of up to ...

Researchers at Boğaziçi University have developed a CycleGAN-based deep learning model with residual dense blocks and attention modules that significantly ...

Researchers have unveiled a generative adversarial network that rebuilds faces obscured by up to 70 percent occlusion while preserving the ...

Researchers combined a graph autoencoder, an attention-equipped graph convolutional network, and a slime-mould-inspired optimizer to locate hidden faults in smart ...

Researchers have developed CHS-Net, a hybrid deep learning architecture that sets a new benchmark for segmenting cracks, erosion, and delamination ...

Researchers have developed a CNN-BiLSTM-Attention hybrid neural network optimized by an improved antlion algorithm that diagnoses and predicts transformer faults ...

Researchers have developed a hyperspectral deep learning framework, STAMPformer, that non-destructively predicts continuous frost damage severity in field-grown tea plants ...

Researchers in China have developed a lightweight transformer model with a novel cross-axis attention module and a feature memory component ...

Researchers have developed a lightweight deep learning framework that grades early sugarcane pokkah boeng disease severity on a smartphone with ...