Object detection as a subfield within computer vision has achieved remarkable progress,
which aims to accurately identify and locate a specific object from images or videos. Such …

The brain-computer interface (BCI) has been investigated as a form of communication tool
between the brain and external devices. BCIs have been extended beyond communication …

In real applications, new object classes often emerge after the detection model has been
trained on a prepared dataset with fixed classes. Fine-tuning the old model with only new …

Recent years have witnessed rapid development and remarkable achievements on deep
learning object detection in remote sensing (RS) images. The growing improvement of the …

The edge detection-based has profoundly inspired recent works in image classification,
object detection, segmentation, et al. With the growth of computer vision, the performance of …

In few-shot object detection (FSOD), many approaches retrain the detector in the inference
stage, which is unrealistic in real applications. Moreover, high-quality region proposals are …

Once only a few-shot annotated samples are available the performance of learning-based
object detection would be heavily dropped. Many few-shot object detection (FSOD) methods …

For the ore particle size detection, obtaining a sizable amount of high-quality ore labeled
data is time-consuming and expensive. General object detection methods often suffer from …

Accurate detection of military targets and their key parts is of great significance for achieving
maximum damage efficiency of attacking drones. However, traditional methods either …

The recent explosive development of knowledge graphs (KGs) in artificial intelligence tasks
coupled with incomplete or partial information has triggered considerable research interest …