To better understand scene images in the field of remote sensing, multi-label annotation of
scene images is necessary. Moreover, to enhance the performance of deep learning models …

Multispectral pedestrian detection has been actively studied as a promising multi-modality
solution to handle illumination and weather changes. Most multi-modality approaches carry …

Since the image related to road damage includes objects such as potholes, cracks,
shadows, and lanes, there is a problem that it is difficult to detect a specific object. In this …

Object detection is a pivotal task for many unmanned aerial vehicle (UAV) applications.
Compared to general scenes, the objects in aerial images are typically much smaller. For …

Given the explosive growth of information technology and the development of computer
vision with convolutional neural networks, wildfire field data information systems are …

Object detection is the most fundamental but challenging issues in the field of computer
vision. Object detection identifies the presence of various individual objects in an image …

Instance segmentation has made great progress in recent years. However, current
mainstream detection-based methods ignore the process of distinguishing different …

Multi-label classification has been used to solve a wide range of problems where each
example in the dataset may be related either to one class (as in traditional classification …

Automatic classification of ECG is very important for early prevention and auxiliary diagnosis
of cardiovascular disease patients. In recent years, many studies based on ECG have …

Multi-label classification is a challenging task, particularly in domains where the number of
labels to be predicted is large. Deep neural networks are often effective at multi-label …