Few-shot segmentation (FSS), which aims to determine specific objects in the query image
given only a handful of densely labeled samples, has received extensive academic attention …

With the ever-growing complexity of models in the field of remote sensing (RS), there is an
increasing demand for solutions that balance model accuracy with computational efficiency …

Object detection in remote sensing images has garnered significant attention due to its wide
applications in real-world scenarios. However, most existing oriented object detectors still …

Targeted Attacks on Object Detection (TAOD) aim to deceive the victim detector into
recognizing a specific instance as the predefined target category while minimizing the …

Recent advancements have significantly improved the efficiency and effectiveness of deep
learning methods for image-based remote sensing tasks. However, the requirement for large …

Few-shot segmentation aims to segment specific objects in a query image based on a few
densely annotated images and has been extensively studied in recent years. In remote …

The semantic segmentation of remote sensing images with few shots has important
theoretical and application value. Most of the existing few-shot semantic segmentation …

Few-shot segmentation (FSS) is proposed to segment unknown class targets with just a few
annotated samples. Most current FSS methods follow the paradigm of mining the semantics …

Few-shot Segmentation aims to segment the interested objects in the query image with just
a handful of labeled samples (ie, support images). Previous schemes would leverage the …

For more efficient generalization to unseen domains (classes), most Few-shot Segmentation
(FSS) would directly exploit pre-trained encoders and only fine-tune the decoder, especially …