Zobrazeno 1 - 10
of 85
pro vyhledávání: '"Mingwen Shao"'
Publikováno v:
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, Vol 16, Pp 10230-10245 (2023)
The global context is crucial to the semantic segmentation task of remote sensing (RS) urban scene imagery since objects have large size variations, high similarity, and mutual occlusion. However, the existing methods for extracting global context in
Externí odkaz:
https://doaj.org/article/9efa5adbf9de4d44b2df77a4ad559c23
A Single-Stage Unsupervised Denoising Low-Illumination Enhancement Network Based on Swin-Transformer
Publikováno v:
IEEE Access, Vol 11, Pp 75696-75706 (2023)
Traditional low-light enhancement methods are often based on paired datasets for training. The training data is difficult to obtain and the resulting model has poor generalization. In unsupervised low-light enhancement networks, because paired data i
Externí odkaz:
https://doaj.org/article/e4f61131dfae4cbb9c3927dd355ea7ea
Publikováno v:
Sensors, Vol 23, Iss 14, p 6446 (2023)
Instance segmentation is a challenging task in computer vision, as it requires distinguishing objects and predicting dense areas. Currently, segmentation models based on complex designs and large parameters have achieved remarkable accuracy. However,
Externí odkaz:
https://doaj.org/article/1ea7411141fc46868e56c0bbee264d8b
Publikováno v:
Applied Sciences, Vol 11, Iss 11, p 5055 (2021)
Due to the characteristics of low signal-to-noise ratio and low contrast, low-light images will have problems such as color distortion, low visibility, and accompanying noise, which will cause the accuracy of the target detection problem to drop or e
Externí odkaz:
https://doaj.org/article/5c967f780d2649b1835e359c4f7a90ec
Publikováno v:
Symmetry, Vol 13, Iss 6, p 950 (2021)
Because small targets have fewer pixels and carry fewer features, most target detection algorithms cannot effectively use the edge information and semantic information of small targets in the feature map, resulting in low detection accuracy, missed d
Externí odkaz:
https://doaj.org/article/3a67bfee572e4587b7908f253474e76b
Publikováno v:
Sensors, Vol 19, Iss 6, p 1476 (2019)
The Adaptive Boosting (AdaBoost) algorithm is a widely used ensemble learning framework, and it can get good classification results on general datasets. However, it is challenging to apply the AdaBoost algorithm directly to imbalanced data since it i
Externí odkaz:
https://doaj.org/article/ce0079984964460d99946c5d74854db2
Publikováno v:
International Journal of Distributed Sensor Networks, Vol 11 (2015)
Neural network is easy to fall into the minimum and overfitting in the application. The paper proposes a novel dynamic weight neural network ensemble model (DW-NNE). The Bagging algorithm generates certain neural network individuals which then are se
Externí odkaz:
https://doaj.org/article/f0fe5e81f3144e28826d8cf212321f6e
Publikováno v:
Computers & Graphics. 112:132-142
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::44c3dc8a24c8c09dad3a31afaa57f40c
https://doi.org/10.1016/j.cag.2023.04.006
https://doi.org/10.1016/j.cag.2023.04.006
Publikováno v:
Information Sciences. 622:424-436
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::4fb2144c6d89d9577cae2b1744f13f09
https://doi.org/10.1016/j.ins.2022.11.140
https://doi.org/10.1016/j.ins.2022.11.140
Publikováno v:
International Journal of Approximate Reasoning. 154:262-276
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::5abd621f7efeea77e306c3294cdf9225
https://doi.org/10.1016/j.ijar.2023.01.001
https://doi.org/10.1016/j.ijar.2023.01.001