Results 41 to 50 of about 4,718,784 (295)
Multi-Attention Bottleneck for Gated Convolutional Encoder-Decoder-Based Speech Enhancement
IEEE Access, 2023 Convolutional encoder-decoder (CED) has emerged as a powerful architecture, particularly in speech enhancement (SE), which aims to improve the intelligibility and quality and intelligibility of noise-contaminated speech.
Nasir Saleem +4 more
doaj +1 more source
A Nested UNet Based on Multi-Scale Feature Extraction for Mixed Gaussian-Impulse Removal
Applied Sciences, 2023 Eliminating mixed noise from images is a challenging task because accurately describing the attenuation of noise distribution is difficult. However, most existing algorithms for mixed noise removal solely rely on the local information of the image and ...
Jielin Jiang +3 more
doaj +1 more source
Semi-Supervised Multi-Channel Speaker Diarization With Cross-Channel Attention
2023 IEEE Automatic Speech Recognition and Understanding Workshop (ASRU), 2023 Most neural speaker diarization systems rely on sufficient manual training data labels, which are hard to collect under real-world scenarios. This paper proposes a semi-supervised speaker diarization system to utilize large-scale multi-channel training data by generating pseudo-labels for unlabeled data.
Shilong Wu +6 more
openaire +3 more sources
2021 16th IEEE International Conference on Automatic Face and Gesture Recognition (FG 2021), 2021 Visual attention has been extensively studied for learning fine-grained features in both facial expression recognition (FER) and Action Unit (AU) detection. A broad range of previous research has explored how to use attention modules to localize detailed facial parts (e,g.
Xiaotian Li +4 more
openaire +3 more sources
Facial Landmark Detection via Attention-Adaptive Deep Network
IEEE Access, 2019 Facial landmark detection is a key component of the face recognition pipeline as well as facial attribute analysis and face verification. Recently convolutional neural network-based face alignment methods have achieved significant improvement, but ...
Muhammad Sadiq +3 more
doaj +1 more source
STCA-SNN: self-attention-based temporal-channel joint attention for spiking neural networks [PDF]
, 2023 Spiking Neural Networks (SNNs) have shown great promise in processing spatio-temporal information compared to Artificial Neural Networks (ANNs). However, there remains a performance gap between SNNs and ANNs, which impedes the practical application of ...
Xiyan Wu +6 more
core +1 more source
The feature aggregation module with channel attention(FAMCA).
, 2023 The feature aggregation module with channel attention(FAMCA).
Ting Wang (16292) +7 more
core +1 more source
Dual Channel Attention Networks
Journal of Physics: Conference Series, 2020 Abstract Channel attention is currently widely used in Computer Vision. Most existing channel attention networks are proposed based on Squeeze-and-Excitation Networks (SE- Net),which can obtain excellent performance by designing complex structures, however, they also has more additional network parameters and higher floating point ...
Jingchen Bian, Yugui Liu
openaire +1 more source
A learnable EEG channel selection method for MI-BCI using efficient channel attention
Frontiers in Neuroscience, 2023 IntroductionDuring electroencephalography (EEG)-based motor imagery-brain-computer interfaces (MI-BCIs) task, a large number of electrodes are commonly used, and consume much computational resources. Therefore, channel selection is crucial while ensuring
Lina Tong +5 more
doaj +1 more source
Pediatric Blood &Cancer, EarlyView.ABSTRACT Background Adult survivors of childhood acute lymphoblastic leukemia (ALL) are at risk of reduced exercise capacity, decreased physical activity levels, persistent fatigue, and neurocognitive deficits. The aim was to investigate exercise capacity and physical activity levels of adult survivors of childhood ALL and their associations with ...
Laura Jess +5 more
wiley +1 more source