Results 11 to 20 of about 290,361 (261)

OrthoNets: Orthogonal Channel Attention Networks

2023 IEEE International Conference on Big Data (BigData), 2023
IEEE BigData ...
Salman, Hadi, Parks, Caleb, Swan, Matthew, Gauch, John   +3 more
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WaveNets: Wavelet Channel Attention Networks

2022 IEEE International Conference on Big Data (Big Data), 2022
IEEE BigData2022 ...
Salman, Hadi, Parks, Caleb, Hong, Shi Yin, Zhan, Justin   +3 more
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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

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

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
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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

Underwater object detection algorithm based on channel attention and feature fusion

Xibei Gongye Daxue Xuebao, 2022
Due to the color deviation, low contrast and fuzzy object in underwater optical images, there are some problems in underwater object detection, such as missed detection and false detection.
ZHANG Yan, LI Xingshan, SUN Yemei, LIU Shudong   +3 more
doaj   +1 more source

A Novel Channel and Temporal-Wise Attention in Convolutional Networks for Multivariate Time Series Classification

IEEE Access, 2020
Multivariate time series classification (MTSC) is a fundamental and essential research problem in the domain of time series data mining. Recently deep neural networks emerged as an end-to-end solution for MTSC and achieve state-of-the-art results on ...
Xu Cheng, Peihua Han, Guoyuan Li, Shengyong Chen, Houxiang Zhang   +4 more
doaj   +1 more source

Self-Supervised Monocular Depth Estimation Based on Channel Attention

Photonics, 2022
Scene structure and local details are important factors in producing high-quality depth estimations so as to solve fuzzy artifacts in depth prediction results.
Bo Tao, Xinbo Chen, Xiliang Tong, Du Jiang, Baojia Chen   +4 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.
Wu, Shilong, Du, Jun, He, Maokui, Niu, Shutong, Chen, Hang, Tang, Haitao, Lee, Chin-Hui   +6 more
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