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CBAM VGG16: An efficient driver distraction classification using CBAM embedded VGG16 architecture.

Computers in biology and medicine
Driver monitoring systems (DMS) are crucial in autonomous driving systems (ADS) when users are concerned about driver/vehicle safety. In DMS, the significant influencing factor of driver/vehicle safety is the classification of driver distractions or ...

Determinantal point process attention over grid cell code supports out of distribution generalization.

eLife
Deep neural networks have made tremendous gains in emulating human-like intelligence, and have been used increasingly as ways of understanding how the brain may solve the complex computational problems on which this relies. However, these still fall ...

An unsupervised multi-view contrastive learning framework with attention-based reranking strategy for entity alignment.

Neural networks : the official journal of the International Neural Network Society
Entity alignment is a crucial task in knowledge graphs, aiming to match corresponding entities from different knowledge graphs. Due to the scarcity of pre-aligned entities in real-world scenarios, research focused on unsupervised entity alignment has...

DGSD: Dynamical graph self-distillation for EEG-based auditory spatial attention detection.

Neural networks : the official journal of the International Neural Network Society
Auditory Attention Detection (AAD) aims to detect the target speaker from brain signals in a multi-speaker environment. Although EEG-based AAD methods have shown promising results in recent years, current approaches primarily rely on traditional conv...

A Multi-Group Multi-Stream attribute Attention network for fine-grained zero-shot learning.

Neural networks : the official journal of the International Neural Network Society
Fine-grained visual categorization in zero-shot setting is a challenging problem in the computer vision community. It requires algorithms to accurately identify fine-grained categories that do not appear during the training phase and have high visual...

Robust visual question answering via polarity enhancement and contrast.

Neural networks : the official journal of the International Neural Network Society
The Visual Question Answering (VQA) task is an important research direction in the field of artificial intelligence, which requires a model that can simultaneously understand visual images and natural language questions, and answer questions related ...

Preparatory activity of anterior insula predicts conflict errors: integrating convolutional neural networks and neural mass models.

Scientific reports
Preparatory brain activity is a cornerstone of proactive cognitive control, a top-down process optimizing attention, perception, and inhibition, fostering cognitive flexibility and adaptive attention control in the human brain. In this study, we prop...

Pre-gating and contextual attention gate - A new fusion method for multi-modal data tasks.

Neural networks : the official journal of the International Neural Network Society
Multi-modal representation learning has received significant attention across diverse research domains due to its ability to model a scenario comprehensively. Learning the cross-modal interactions is essential to combining multi-modal data into a joi...

Cognitive and behavioral markers for human detection error in AI-assisted bridge inspection.

Applied ergonomics
Integrating Artificial Intelligence (AI) and drone technology into bridge inspections offers numerous advantages, including increased efficiency and enhanced safety. However, it is essential to recognize that this integration changes the cognitive er...

LGGNet: Learning From Local-Global-Graph Representations for Brain-Computer Interface.

IEEE transactions on neural networks and learning systems
Neuropsychological studies suggest that co-operative activities among different brain functional areas drive high-level cognitive processes. To learn the brain activities within and among different functional areas of the brain, we propose local-glob...