5 accepted papers
Deep learning approaches for marine fog detection and forecasting have outperformed traditional methods, demonstrating significant scientific and practical importance. However, the limited availability of open-source datasets remains a major challenge. Existing datasets, often focused on a single re…
Image stitching in heavy occlusion scenarios faces the dual challenges of accurate alignment and occlusion removal. On one hand, occlusion causes the loss of key texture and structural information in the image. On the other hand, it affects the image’s integrity. Existing stitching methods perform w
"Since the future of computing is heterogeneous, scalability is a crucial problem for single image super-resolution. Recent works try to train one network, which can be deployed on platforms with different capacities. However, they rely on the pixel-wise sparse convolution, which is not hardware-fri…
The realistic simulation of underwater scenes has important significance for many researches related to underwater vision, such as underwater image restoration, underwater moving object monitoring, etc. To date, however, the existing underwater scene simulation pipelines are either too complicated d…
Internet video delivery has undergone a tremendous explosion of growth over the past few years. However, the quality of video delivery system greatly depends on the Internet bandwidth. Deep Neural Networks (DNNs) are utilized to improve the quality of video delivery recently. These methods divide a…