The rapid development of the Vision Transformer backbones has enabled the capture of
feature information with global dependencies, leading to excellent performance in salient …

Reservoir elastic parameters play an important role in resource exploration; however, the
band-limited characteristics of seismic data and the ill-posed nature of seismic inversion …

Deep convolutional neural network (DCNN)-based methods have shown great
improvements in building extraction from high spatial resolution remote sensing images. In …

Automatic image colourisation studies how to colourise greyscale images. Existing
approaches exploit convolutional layers that extract image-level features learning the …

Underwater images are fundamental for studying and understanding the status of marine
life. We focus on reducing the memory space required for image storage while the memory …

Based on Fully Convolutional Networks, recent salient object detection (SOD) methods
achieve impressive results. Some studies improve the classical SOD frameworks by utilizing …

The rise of Deep Neural Networks (DNNs) has significantly boosted salient object detection
in computer vision tasks. However, downsampling methods like striding and pooling often …

Automatic image colourisation is the computer vision research path that studies how to
colourise greyscale images (for restoration). Deep learning techniques improved image …