AIMC Topic: Attention

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Unsupervised Accuracy Estimation for Brain-Computer Interfaces Based on Selective Auditory Attention Decoding.

IEEE transactions on bio-medical engineering
OBJECTIVE: Selective auditory attention decoding (AAD) algorithms process brain data such as electroencephalography to decode to which of multiple competing sound sources a person attends. Example use cases are neuro-steered hearing aids or communica...

A novel STA-EEGNet combined with channel selection for classification of EEG evoked in 2D and 3D virtual reality.

Medical engineering & physics
Virtual reality (VR), particularly through 3D presentations, significantly boosts user engagement and task efficiency in fields such as gaming, education, and healthcare, offering more immersive and interactive experiences than traditional 2D formats...

Multi-level semantic-aware transformer for image captioning.

Neural networks : the official journal of the International Neural Network Society
Effective visual representation is crucial for image captioning task. Among the existing methods, the grid-based visual encoding methods take fragmented features extracted from the entire image as input, lacking the fine-grained semantic information ...

Semantic information-based attention mapping network for few-shot knowledge graph completion.

Neural networks : the official journal of the International Neural Network Society
Few-shot Knowledge Graph Completion (FKGC), an emerging technology capable of inferring new triples using only a few reference relation triples, has gained significant attention in recent years. However, existing FKGC methods primarily focus on struc...

CFI-Former: Efficient lane detection by multi-granularity perceptual query attention transformer.

Neural networks : the official journal of the International Neural Network Society
Benefiting from the booming development of Transformer methods, the performance of lane detection tasks has been rapidly improved. However, due to the influence of inaccurate lane line shape constraints, the query sequences of existing transformer-ba...

A spatial-spectral fusion convolutional transformer network with contextual multi-head self-attention for hyperspectral image classification.

Neural networks : the official journal of the International Neural Network Society
Convolutional neural networks (CNNs) can effectively extract local features, while Vision Transformer excels at capturing global features. Combining these two networks to enhance the classification performance of hyperspectral images (HSI) has garner...

Self-attention fusion and adaptive continual updating for multimodal federated learning with heterogeneous data.

Neural networks : the official journal of the International Neural Network Society
Federated learning (FL) enables collaborative model training without direct data sharing, facilitating knowledge exchange while ensuring data privacy. Multimodal federated learning (MFL) is particularly advantageous for decentralized multimodal data,...

Enhancing few-shot image classification through learnable multi-scale embedding and attention mechanisms.

Neural networks : the official journal of the International Neural Network Society
In the context of few-shot classification, the goal is to train a classifier using a limited number of samples while maintaining satisfactory performance. However, traditional metric-based methods exhibit certain limitations in achieving this objecti...

Attribute-guided feature fusion network with knowledge-inspired attention mechanism for multi-source remote sensing classification.

Neural networks : the official journal of the International Neural Network Society
Land use and land cover (LULC) classification is a popular research area in remote sensing. The information of single-modal data is insufficient for accurate classification, especially in complex scenes, while the complementarity of multi-modal data ...

CNNs improve decoding of selective attention to speech in cochlear implant users.

Journal of neural engineering
. Understanding speech in the presence of background noise such as other speech streams is a difficult problem for people with hearing impairment, and in particular for users of cochlear implants (CIs). To improve their listening experience, auditory...