Results 21 to 30 of about 26,776 (176)
Ship classification using the synthetic aperture radar (SAR) images has a significant role in remote sensing applications. Aiming at the problems of excessive model parameters numbers and high energy consumption in the traditional deep learning methods ...
Xinqiao Jiang +3 more
doaj +1 more source
Sleep staging is of critical significance to the diagnosis of sleep disorders, and the electroencephalogram (EEG), which is used for monitoring brain activity, is commonly employed in sleep staging.
Yuyang You +3 more
doaj +1 more source
Siamese tracking network with multi-attention mechanism
Abstract Object trackers based on Siamese networks view tracking as a similarity-matching process. However, the correlation operation operates as a local linear matching process, limiting the tracker's ability to capture the intricate nonlinear relationship between the template and search region branches.
Yuzhuo Xu +4 more
openaire +1 more source
DASTSiam: Spatio‐temporal fusion and discriminative enhancement for Siamese visual tracking
The use of deep neural networks has revolutionised object tracking tasks, and Siamese trackers have emerged as a prominent technique for this purpose.
Yucheng Huang +6 more
doaj +1 more source
Semi-supervised Learning Using Siamese Networks [PDF]
added link of GitHub ...
Sahito, Attaullah +2 more
openaire +2 more sources
Re-Identification With Consistent Attentive Siamese Networks [PDF]
10 pages, 8 figures, 3 tables, to appear in CVPR ...
Zheng, Meng +3 more
openaire +2 more sources
Few Shot Learning in Histopathological Images:Reducing the Need of Labeled Data on Biological Datasets [PDF]
Although deep learning pathology diagnostic algorithms are proving comparable results with human experts in a wide variety of tasks, they still require a huge amount of well annotated data for training.
Belar, Oihana +7 more
core +1 more source
KD-Fixmatch: Knowledge Distillation Siamese Neural Networks
Semi-supervised learning (SSL) has become a crucial approach in deep learning as a way to address the challenge of limited labeled data. The success of deep neural networks heavily relies on the availability of large-scale high-quality labeled data. However, the process of data labeling is time-consuming and unscalable, leading to shortages in labeled ...
Wang, Chien-Chih +4 more
openaire +2 more sources
Exploring Recurrent Neural Networks for On-Line Handwritten Signature Biometrics
Systems based on deep neural networks have made a breakthrough in many different pattern recognition tasks. However, the use of these systems with traditional architectures seems not to work properly when the amount of training data is scarce.
Ruben Tolosana +3 more
doaj +1 more source
A Siamese Network Based U-Net for Change Detection in High Resolution Remote Sensing Images
Remote sensing image change detection (RSICD) is a technique that explores the change of surface coverage in a certain time series by studying the difference between multiple remote sensing images (RSIs) collected over the same area.
Tao Chen +5 more
doaj +1 more source

