Federated Learning is a novel framework that allows multiple devices or institutions to train a
machine learning model collaboratively while preserving their data private. This …

Convolutional neural networks (CNNs) have recently shown excellent performance on the
task of fine-grained vehicle classification, where the motivation is to identify the fine-grained …

A common problem in pixelwise classification or semantic segmentation is class imbalance,
which tends to reduce the classification accuracy of minority-class regions. An effective way …

Along with the growing demands of intelligent traffic system, how to recognize the category
information of a car from surveillance cameras has been an important task. Fine-grained car …

Intelligent fault diagnosis methods, especially convolutional neural network (CNN), have
made significant progress in gas-insulated switchgear (GIS) partial discharge (PD) …

Extracting vehicle information is of great significance to the construction of the Internet of
Vehicles (IoV). Vehicle logo detection (VLD) technology can effectively extract vehicle …

In conventional robot-assisted minimally invasive procedures (RMIS), surgeons have narrow
visual and complex working spaces, along with specular reflection, blood, camera-lens …

Incorporating higher-order optimization functions, such as Levenberg–Marquardt (LM) have
revealed better generalizable solutions for deep learning problems. However, these higher …

Due to lack of data, overfitting ubiquitously exists in real-world applications of deep neural
networks (DNNs). We propose advanced dropout, a model-free methodology, to mitigate …

Existing feature models used in thermal infrared (TIR) tracking struggle to get strong
discriminative features of TIR objects, because TIR image has few details and low contrast …