Enhancing Aerial Object Detection with Selective Frequency Interaction Network

Engineering, Aerospace Engineering, Information Science, Computer Science, Artificial Intelligence,
aerial object detection   frequency-domain features   selective frequency interaction   convolutional neural networks   feature extraction   deep learning   computer vision  

Selective Frequency Interaction Network for Improved Aerial Object Detection Background and Problem Statement With the advancements in computer vision, aerial object detection has become a critical research focus in remote sensing. This task aims to identify targets such as vehicles or buildings from aerial images captured at varying angles and alt...

Reliable Evaluation of Attribution Maps in CNNs: A Perturbation-Based Approach

Information Science, Electronic Information, Computer Science, Automation, Artificial Intelligence,
attribution maps   perturbation evaluation   explainability   convolutional neural networks   smoothness   monotonicity   baseline checks  

Deep Learning Explainability Research: A Perturbation-Based Evaluation Method for Attribution Maps Background and Motivation With the remarkable success of deep learning models across various tasks, there is growing attention on the interpretability and transparency of these models. However, while these models excel in accuracy, their decision-maki...

DeepSleepNet: A Model for Automatic Sleep Stage Scoring Based on Raw Single-Channel EEG

Information Science, Computer Science, Artificial Intelligence, Medicine, Medical Laboratory Science, Neurology,
sleep stage scoring   deep learning   single-channel EEG   convolutional neural networks   bidirectional LSTMs   feature extraction   automation  

DeepSleepNet: An Automatic Sleep Stage Scoring Model Based on Single-Channel EEG Background Introduction Sleep has a significant impact on human health, and monitoring sleep quality is crucial in medical research and practice. Typically, sleep experts score sleep stages by analyzing various physiological signals such as electroencephalogram (EEG), ...

Multi-Feature Attention Convolutional Neural Network for Motor Imagery Decoding

Engineering, Electrical Engineering, Information Science, Automation, Artificial Intelligence, Medicine, Medical Laboratory Science, Neurology, Life Sciences, Bioengineering,
multi-feature attention   convolutional neural networks   motor imagery   brain-computer interface   lightweight model  

Brain-Computer Interface (BCI) is a communication method that connects the nervous system to the external environment. Motor Imagery (MI) is the cornerstone of BCI research, referring to the internal rehearsal before physical execution. Non-invasive techniques such as Electroencephalography (EEG) can record neural activities with high temporal reso...

Efficient Tensor Decomposition-Based Filter Pruning

Information Science, Automation, Artificial Intelligence,
network compression   tensor decompositions   filter pruning   convolutional neural networks   low-rank approximation  

Background Introduction Network Pruning is a crucial technique for designing efficient Convolutional Neural Network (CNN) models. By reducing memory footprint and computational demands, while maintaining or improving overall performance, it makes deploying CNNs on resource-constrained devices (such as mobile phones or embedded systems) feasible. Th...