Zobrazeno 1 - 10
of 140
pro vyhledávání: '"Dual-Encoder"'
Publikováno v:
IEEE Access, Vol 12, Pp 153851-153858 (2024)
Deep learning models have significantly addressed the challenges of multivariate time series forecasting. Recently, Transformer-based models which have primarily focused on either temporal or inter-variate (spatial) dependencies have demonstrated exc
Externí odkaz:
https://doaj.org/article/c816f68e705a4bf5b6165d3b40f5a7e9
Publikováno v:
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, Vol 17, Pp 15914-15926 (2024)
Semantic segmentation of large-scale point clouds is essential for applications such as autonomous driving and high-definition mapping. However, this task remains challenging due to the imbalanced distribution of categories in large-scale point cloud
Externí odkaz:
https://doaj.org/article/eab0f0f0405f4af8b64491655a3fba4e
Publikováno v:
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, Vol 17, Pp 7410-7421 (2024)
The extraction of buildings from synthetic aperture radar (SAR) images poses a challenging task in the realm of remote sensing (RS). In recent years, convolutional neural networks (CNNs) have rapidly advanced and found application in the field of RS.
Externí odkaz:
https://doaj.org/article/86b872d88f054d919fef77a963f12067
Publikováno v:
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, Vol 17, Pp 2372-2385 (2024)
Although the vision transformer-based methods (ViTs) exhibit an excellent performance than convolutional neural networks (CNNs) for image recognition tasks, their pixel-level semantic segmentation ability is limited due to the lack of explicit utiliz
Externí odkaz:
https://doaj.org/article/7d90fc211e244210824f375b8511e4d2
Publikováno v:
Remote Sensing, Vol 16, Iss 14, p 2603 (2024)
Understanding the distribution of rock glaciers provides key information for investigating and recognizing the status and changes of the cryosphere environment. Deep learning algorithms and red–green–blue (RGB) bands from high-resolution satellit
Externí odkaz:
https://doaj.org/article/75120df0004a4bbf8b221af9d5f92dcc
Publikováno v:
Applied Sciences, Vol 14, Iss 11, p 4834 (2024)
Segmentation methods based on convolutional neural networks (CNN) have achieved remarkable results in the field of medical image segmentation due to their powerful representation capabilities. However, for brain-tumor segmentation, owing to the signi
Externí odkaz:
https://doaj.org/article/32866e2b4d7640a7b998c742565ea41a
Publikováno v:
Geocarto International, Vol 39, Iss 1 (2024)
Transformer models boost building extraction accuracy by capturing global features from images. However, convolutional networks’ potential in local feature extraction remains underutilized in CNN + Transformer models, limiting performance. To harne
Externí odkaz:
https://doaj.org/article/efb5e07d17b5493680fbca6fd2e58bba
Publikováno v:
GIScience & Remote Sensing, Vol 60, Iss 1 (2023)
Automatic surface water body mapping using remote sensing technology is greatly meaningful for studying inland water dynamics at regional to global scales. Convolutional neural networks (CNN) have become an efficient semantic segmentation technique f
Externí odkaz:
https://doaj.org/article/6f8741a8805344288a1a8027a68a20a6
Publikováno v:
IEEE Transactions on Neural Systems and Rehabilitation Engineering, Vol 31, Pp 2018-2027 (2023)
The current data scarcity problem in EEG-based emotion recognition tasks leads to difficulty in building high-precision models using existing deep learning methods. To tackle this problem, a dual encoder variational autoencoder-generative adversarial
Externí odkaz:
https://doaj.org/article/ddb7cf71ca7d40aa809fef13e18571fd
Publikováno v:
Applied Sciences, Vol 14, Iss 8, p 3259 (2024)
In Cone Beam Computed Tomography (CBCT) images, accurate tooth segmentation is crucial for oral health, providing essential guidance for dental procedures such as implant placement and difficult tooth extractions (impactions). However, due to the lac
Externí odkaz:
https://doaj.org/article/1b9cc6d0f92e452a82fd186e1c5e7d0f