AIMC Topic: Attention

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InBRwSANet: Self-attention based parallel inverted residual bottleneck architecture for human action recognition in smart cities.

PloS one Jan 1, 2025
Human Action Recognition (HAR) has grown significantly because of its many uses, including real-time surveillance and human-computer interaction. Various variations in routine human actions make the recognition process of action more difficult. In th...
Humans Deep Learning Cities Attention Neural Networks, Computer Algorithms
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Scene-dependent sound event detection based on multitask learning with deformable large kernel attention convolution.

PloS one Jan 1, 2025
Sound event detection (SED) and acoustic scene classification (ASC) are closely related tasks in environmental sound analysis. Given the interrelationship between sound events and scenes, some previous studies have proposed using the multitask learni...
Algorithms Acoustics Humans Machine Learning Sound Attention
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Combining Real-Time Neuroimaging With Machine Learning to Study Attention to Familiar Faces During Infancy: A Proof of Principle Study.

Developmental science Jan 1, 2025
Looking at caregivers' faces is important for early social development, and there is a concomitant increase in neural correlates of attention to familiar versus novel faces in the first 6 months. However, by 12 months of age brain responses may not d...
Humans Machine Learning Brain Electroencephalography Recognition, Psychology Attention Evoked Potentials Face Facial Recognition Neuroimaging Infant Female Bayes Theorem Male
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Human Eyes-Inspired Recurrent Neural Networks Are More Robust Against Adversarial Noises.

Neural computation Aug 19, 2024
Humans actively observe the visual surroundings by focusing on salient objects and ignoring trivial details. However, computer vision models based on convolutional neural networks (CNN) often analyze visual input all at once through a single feedforw...
Neural Networks, Computer Attention Visual Perception Fixation, Ocular Humans
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A Sentiment Pre-trained Text-Guided Multimodal Cross-Attention Transformer for Improved Depression Detection.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference Jul 1, 2024
Depression is a widespread mental health issue requiring efficient automated detection methods. Traditional single-modality approaches are less effective due to the disorder's complexity, leading to a focus on multimodal analysis. Recent advancements...
Humans Natural Language Processing Algorithms Depression Attention Emotions
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The Impact of Cross-Validation Schemes for EEG-Based Auditory Attention Detection with Deep Neural Networks.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference Jul 1, 2024
This study assesses the performance of different cross-validation splits for brain-signal-based Auditory Attention Decoding (AAD) using deep neural networks on three publicly available Electroencephalography datasets. We investigate the effect of tri...
Humans Neural Networks, Computer Brain Reproducibility of Results Signal Processing, Computer-Assisted Deep Learning Attention Electroencephalography
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An Attention-Based Hybrid Deep Learning Approach for Patient-Specific, Cross-Patient, and Patient-Independent Seizure Detection.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference Jul 1, 2024
Automatic detection of epilepsy plays a crucial role in diagnosing and treatment of patients, while most current methods rely on patient-specific models and have shown promising results, which is not suitable for clinical application, especially when...
Attention Seizures Signal Processing, Computer-Assisted Humans Deep Learning Neural Networks, Computer Algorithms Electroencephalography
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[A medical visual question answering approach based on co-attention networks].

Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi Jun 25, 2024
Recent studies have introduced attention models for medical visual question answering (MVQA). In medical research, not only is the modeling of "visual attention" crucial, but the modeling of "question attention" is equally significant. To facilitate ...
Humans Attention Neural Networks, Computer Algorithms
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Deep Depression Detection with Resting-State and Cognitive-Task EEG.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference Jul 1, 2023
Depression is a common mental disorder that negatively affects physical health and personal, social and occupational functioning. Currently, accurate and objective diagnosis of depression remains challenging, and electroencephalography (EEG) provides...
Attention Cognition Electroencephalography Humans Neural Networks, Computer Depression
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SADeepcry: a deep learning framework for protein crystallization propensity prediction using self-attention and auto-encoder networks.

Briefings in bioinformatics Sep 20, 2022
The X-ray diffraction (XRD) technique based on crystallography is the main experimental method to analyze the three-dimensional structure of proteins. The production process of protein crystals on which the XRD technique relies has undergone multiple...
Crystallography, X-Ray Attention Deep Learning Proteins Crystallization
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