Convolutional neural networks (CNNs) for hyperspectral image (HSI) classification have
generated good progress. Meanwhile, graph convolutional networks (GCNs) have also …

Graph convolutional network (GCN) as a combination of deep learning (DL) and graph
learning has gained increasing attention in hyperspectral image (HSI) classification …

Image dehazing is a challenging task in computer vision. Currently, most dehazing methods
adopt the U-Net architecture that directly fuses the decoding layer with the corresponding …

Hyperspectral image (HSI) anomaly detection (AD) generally considers background pixels
as low-rank distribution and anomaly pixels as sparse distribution. However, it is usually …

Recently, CycleGAN-based methods have been widely applied to the unsupervised image
dehazing and achieved significant results. However, most existing CycleGAN-based …

Benefiting from deep learning, Salient Object Detection (SOD) has made much progress.
However, most existing methods adopt the same strategy to extract salient cues from …

Abstract Unsupervised Video Object Segmentation (VOS) aims at identifying the contours of
primary foreground objects in videos without any prior knowledge. However, previous …

Learned image compression has achieved a series of breakthroughs for nature images, but
there is little literature focusing on high-resolution remote sensing image (HRRSI) datasets …

Few-shot face recognition under occlusion (FSFRO) aims to recognize novel subjects given
only a few, probably occluded face images, and it is challenging and common in real-world …

The YOLO series of algorithms have made substantial contributions to the detection of
insulator defects in power transmission line operations. However, existing target detection …