Results 1 to 10 of about 324,498 (117)

Hybrid channel attention network for auditory attention detection [PDF]

Scientific Reports
Humans exhibit a remarkable ability to selectively focus on auditory stimuli in multi-speaker environments, such as cocktail parties. The Auditory Attention Detection (AAD) method aims to identify the conversation that a listener is attending to through ...
Yahao Wen, Shuai Ma, Chuang Liu, Yongjie Wang   +3 more
doaj   +2 more sources

Locally Adaptive Channel Attention-Based Network for Denoising Images

IEEE Access, 2020
Channel attention has recently been proposed and shown a great improvement in image classification accuracy. In this paper, we show that channel attention can greatly help a low-level vision task, image denoising, as well, and propose channel attention ...
Haeyun Lee, Sunghyun Cho
doaj   +3 more sources

Heart Sound Classification Based on Multi-Scale Feature Fusion and Channel Attention Module [PDF]

Bioengineering
Intelligent heart sound diagnosis based on Convolutional Neural Networks (CNN) has been attracting increasing attention due to its accuracy and efficiency, which have been improved by recent studies.
Mingzhe Li, Zhaoming He, Hao Wang
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Multibranch Attention Mechanism Based on Channel and Spatial Attention Fusion

Mathematics, 2022
Recently, it has been demonstrated that the performance of an object detection network can be improved by embedding an attention module into it. In this work, we propose a lightweight and effective attention mechanism named multibranch attention (M3Att).
Guojun Mao, Guanyi Liao, Hengliang Zhu, Bo Sun   +3 more
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EMCA: Efficient Multiscale Channel Attention Module

IEEE Access, 2022
Attention mechanisms have been explored with CNNs across the spatial and channel dimensions. However, all the existing methods devote the attention modules to capture local interactions from a uni-scale. This paper tackles the following question: can one
Eslam Mohamed Bakr, Ahmad El-Sallab, Mohsen Rashwan   +2 more
doaj   +1 more source

Channel-Attention U-Net: Channel Attention Mechanism for Semantic Segmentation of Esophagus and Esophageal Cancer

IEEE Access, 2020
The effective segmentation of esophagus and esophageal cancer from Computed Tomography (CT) images can meaningfully assist doctors in the diagnosis and treatment of esophageal cancer patients.
Guoheng Huang, Junwen Zhu, Jiajian Li, Zhuowei Wang, Lianglun Cheng, Lizhi Liu, Haojiang Li, Jian Zhou   +7 more
doaj   +1 more source

MAS-UNet: a U-shaped network for prostate segmentation

Frontiers in Medicine, 2023
Prostate cancer is a common disease that seriously endangers the health of middle-aged and elderly men. MRI images are the gold standard for assessing the health status of the prostate region.
YuQi Hong, Zhao Qiu, Huajing Chen, Bing Zhu, Haodong Lei   +4 more
doaj   +1 more source

Object Tracking Based on Channel Attention

IEEE Access, 2020
Object tracking is one of the important research topics in computer vision. Despite the great progress in this area, effectively and efficiently tracking object in videos still remains challenging especially in scenarios of rapid movement of objects ...
Zhiquan He, Xuejun Chen
doaj   +1 more source

IRE: Improved Image Super-Resolution Based on Real-ESRGAN

IEEE Access, 2023
Image super-resolution (SR) is a research field focusing on image degradation techniques. The High-order Deterioration Model (HDM) implemented in Real-ESRGAN has proven more effective in simulating the degradation of real-world images compared to ...
Zhengwei Zhu, Yushi Lei, Yilin Qin, Chenyang Zhu, Yanping Zhu   +4 more
doaj   +1 more source

iEnhancer-DCSV: Predicting enhancers and their strength based on DenseNet and improved convolutional block attention module

Frontiers in Genetics, 2023
Enhancers play a crucial role in controlling gene transcription and expression. Therefore, bioinformatics puts many emphases on predicting enhancers and their strength.
Jianhua Jia, Rufeng Lei, Lulu Qin, Genqiang Wu, Xin Wei   +4 more
doaj   +1 more source