Tag: self-attention

Researchers have combined an Enhanced-Value attention scheme within a Swin Transformer with super-resolution-guided training to improve detection of tiny objects ...

Researchers at Harbin Normal University have developed WFGformer, a dual-branch transformer network that uses Haar wavelet frequency decomposition to restore ...

A controlled head-to-head study shows Vision Transformers beat convolutional networks on real X-ray diffraction data once fine-tuned, while CNNs remain ...

Researchers have developed a transformer-based framework with local-global attention that identifies writers from scanned handwriting without reading the text, achieving ...

A new hybrid model called TG-Parser combines Transformer encoders with graph convolutional networks to achieve record-setting accuracy in English grammatical ...

Researchers in India combined EfficientNet-B0 and a transformer encoder to classify Alzheimer's disease from multi-slice MRI scans, achieving 88 percent ...

Researchers in China have developed RAUD-MAPPO, a role-aware and uncertainty-driven multi-agent reinforcement learning framework that raises win rates and enables ...

Researchers in Beijing have built a 0.27-megabyte transformer model that detects multi-stage APT attacks in industrial IoT telemetry with high ...

Researchers have developed DIRA, a model that combines dynamic entity embeddings with implicit relation-aware self-attention to significantly improve temporal knowledge ...

Researchers have developed a parallel long short-term memory network with self-attention that reaches 91.6 percent accuracy in recognizing sentiment in ...