Multispectral object detection faces challenges in effectively integrating complementary information from different modalities in complex environmental conditions. This paper proposes SDRFPT-Net (Spectral Dual-stream Recursive Fusion Perception Targe...
In recent years, deep convolutional neural networks (CNNs) have made significant progress in single-image super-resolution (SISR) tasks. Despite their good performance, the single-image super-resolution task remains a challenging one due to problems...
Image capture devices capture poor-quality images under low-light conditions, and the resulting images have dark areas due to insufficient exposure. Traditional Multiple Exposure Fusion (MEF) methods fuse images with different exposure levels from a...
Camouflaged object detection (COD) seeks to precisely detect and delineate objects that are concealed within complex and ambiguous backgrounds. However, due to subtle texture variations and semantic ambiguity, it remains a highly challenging task. Ex...
With the rapid development of multimodal prompt learning in unsupervised domains, prompt tuning has demonstrated significant potential for dense counting tasks. However, existing supervised methods heavily rely on annotated data, limiting their gener...
With the significant increase in solar power generation’s proportion in power systems, the uncertainty of its power output poses increasingly severe challenges to grid operation. In recent years, solar forecasting models have achieved remarkabl...
Background: Stigma is a pervasive, though understudied, psychosocial factor affecting people with multiple sclerosis. This review synthesizes the literature on the impact of perceived, enacted, and internalized stigma on psychological health and illn...
Timely and accurate land use information in open-pit mines is essential for environmental monitoring, ecological restoration planning, and promoting sustainable progress in mining regions. This study used high-resolution unmanned aerial vehicle (UAV)...