AIMC Topic: Attention

Clear Filters Showing 61 to 70 of 642 articles

Predicting occupant response curves in vehicle crashes via Attention-enhanced multimodal temporal Network.

Accident; analysis and prevention
Accurately predicting safety responses, especially occupant crash response curves across multiple body regions, plays a crucial role in advancing vehicle crash safety by enabling design optimization and reducing the reliance on costly physical testin...

Foster noisy label learning by exploiting noise-induced distortion in foreground localization.

Neural networks : the official journal of the International Neural Network Society
Large-scale, well-annotated datasets are crucial for training deep neural networks. However, the prevalence of noisy-labeled samples can cause irreversible impairment to the generalization of models. Existing approaches have attempted to mitigate the...

Cognitive Lab: A dataset of biosignals and HCI features for cognitive process investigation.

Computer methods and programs in biomedicine
BACKGROUND AND OBJECTIVE: Attention, cognitive workload/fatigue, and emotional states significantly influence learning outcomes, cognitive performance, and human-machine interactions. However, existing assessment methodologies fail to fully capture t...

Single-microphone deep envelope separation based auditory attention decoding for competing speech and music.

Journal of neural engineering
In this study, we introduce an end-to-end single microphone deep learning system for source separation and auditory attention decoding (AAD) in a competing speech and music setup. Deep source separation is applied directly on the envelope of the obse...

SMANet: A Model Combining SincNet, Multi-Branch Spatial-Temporal CNN, and Attention Mechanism for Motor Imagery BCI.

IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society
Building a brain-computer interface (BCI) based on motor imagery (MI) requires accurately decoding MI tasks, which poses a significant challenge due to individual discrepancy among subjects and low signal-to-noise ratio of EEG signals. We propose an ...

Self-supervised spatial-temporal contrastive network for EEG-based brain network classification.

Neural networks : the official journal of the International Neural Network Society
Electroencephalogram (EEG)-based brain network analysis has shown promise in brain disease research by revealing the complex connectivity among brain regions. However, existing methods struggle to fully utilize the large amounts of unlabeled data to ...

MSCViT: A small-size ViT architecture with multi-scale self-attention mechanism for tiny datasets.

Neural networks : the official journal of the International Neural Network Society
Vision Transformer (ViT) has demonstrated significant potential in various vision tasks due to its strong ability in modeling long-range dependencies. However, such success is largely fueled by training on massive samples. In real applications, the l...

Multi-modal sentiment recognition with residual gating network and emotion intensity attention.

Neural networks : the official journal of the International Neural Network Society
Multimodal emotion recognition focuses on the prediction of emotions using text, visual and acoustic modalities, and some results have been generated in this field. Previous approaches fall short in two aspects, one is the processing of complementary...

Developmental coordination disorder and cerebral visual impairment: What is the association?

Research in developmental disabilities
INTRODUCTION: Children with Developmental Coordination Disorder (DCD) experience impairments beyond motor planning, affecting visual perceptual and visual-motor integration abilities, similar to children with Cerebral Visual Impairment (CVI), making ...

AdamGraph: Adaptive Attention-Modulated Graph Network for EEG Emotion Recognition.

IEEE transactions on cybernetics
The underlying time-variant and subject-specific brain dynamics lead to inconsistent distributions in electroencephalogram (EEG) topology and representations within and between individuals. However, current works primarily align the distributions of ...