Remote sensing object detection (RSOD), one of the most fundamental and challenging
tasks in the remote sensing field, has received long-standing attention. In recent years, deep …

Salient object detection (SOD) in optical remote sensing images (RSIs) is a valuable and
challenging task. Although many SOD methods for RSIs have been proposed, there are still …

Object detection is a fundamental task in remote sensing image analysis and scene
understanding. Previous remote sensing object detectors are typically based on …

YOLOv5 is one of the most popular object detection algorithms, which is divided into multiple
series according to the control of network depth and width. To realize the deployment of …

Scene classification aims to classify various objects and land use classes such as farms,
highways, rivers, and airplanes in the remote sensing images. In recent times, the …

Existing methods for Salient Object Detection in Optical Remote Sensing Images (ORSI-
SOD) mainly adopt Convolutional Neural Networks (CNNs) as the backbone, such as VGG …

Current state-of-the-art remote sensing salient object detectors always require high-
resolution spatial context to ensure excellent performance, which incurs enormous …

Convolutional neural networks (CNNs) have been demonstrated to be powerful tools to
automatically learn effective features from large datasets. Though features learned in CNNs …

Early detection is essential for cervical cancer therapy, which is the fourth most frequent
malignancy worldwide. While the Pap smear test is the established approach for identifying …

The application of optical remote sensing images (ORSIs) is prevalent in many fields.
Accordingly, ORSI-oriented salient object detection (SOD) has attracted more attention in …