Deep reinforcement learning (DRL) provides a new solution for autonomous robotic path
planning in a known indoor environment. Previous studies mainly focused on robot path …

This study proposes a novel approach for detecting weak small infrared (IR) targets, called
double-image and double-local contrast measurement (DDLCM), designed to overcome …

The visual interpretation of infrared images is often hindered by low contrast and a limited
dynamic range. This poses a significant challenge as existing enhancement methods are …

Although machine translation has received great progress in recent years, machine
translation results usually existed some errors due to the complex relationship between …

Infrared small target detection faces various challenges such as long distances, weak
features, and small scales. While methodologies based on convolutional neural networks …

In recent years, graph neural networks (GNNs) for few-shot learning have garnered
significant attention due to their powerful learning capabilities. However, previous methods …

Infrared object detection constitutes a significant ship-targeting methodology, exerting a vital
role in maritime safety. The contemporary research regarding infrared ship imagery is …

The assembly of fastening components traditionally relies on labour-intensive human-
machine collaboration, which incurs high costs. Existing methods often assume fixed …

It remains important to make abnormity detection from largescale behavioral data of Internet.
Existing related approaches mostly failed to employ high-dimensional characteristics of …

With the development of computer vision, automatic gesture recognition towards sports
scenarios has been more significant in recent years. However, due to the complexity of …