Deep learning-based algorithms have seen a massive popularity in different areas of remote
sensing image analysis over the past decade. Recently, transformer-based architectures …

Object detection in remote sensing images (RSIs) requires the locating and classifying of
objects of interest, which is a hot topic in RSI analysis research. With the development of …

Transformer, first applied to the field of natural language processing, is a type of deep neural
network mainly based on the self-attention mechanism. Thanks to its strong representation …

Transformer, first applied to the field of natural language processing, is a type of deep neural
network mainly based on the self-attention mechanism. Thanks to its strong representation …

Object detection is the most important problem in computer vision tasks. After AlexNet
proposed, based on Convolutional Neural Network (CNN) methods have become …

Feature pyramid networks are widely applied in remote sensing images for object detection
to deal with the challenge of large scale variation in objects. However, the feature pyramid …

Transformers have rapidly gained popularity in computer vision, especially in the field of
object recognition and detection. Upon examining the outcomes of state-of-the-art object …

Existing oriented object detection methods commonly use metric AP $ _ {50} $ to measure
the performance of the model. We argue that AP $ _ {50} $ is inherently unsuitable for …

Remote sensing scene understanding is crucial for extracting valuable information from high-
resolution images, including object detection and classification. Traditional object detection …

Frequent and accurate object detection based on remote sensing images is an encouraging
approach for monitoring dynamic of the interest object on earth surface. Transformer-based …