With the gallo** progress of the Internet of Things (IoT) and related technologies in
multiple facets of science, distribution environments, namely cloud, edge, fog, Internet of …

The influence of digital industry and firm digitization on enterprise innovation has emerged
as a critical research topic. To assess the impact of digital transformation on enhancing …

This paper reviews the 1st LFNAT challenge on light field depth estimation, which aims at
predicting disparity information of central view image in a light field (ie, pixel offset between …

Multimodal fusion can make semantic segmentation more robust. However, fusing an
arbitrary number of modalities remains underexplored. To delve into this problem, we create …

Global context information is particularly important for comprehensive scene understanding.
It helps clarify local confusions and smooth predictions to achieve fine-grained and coherent …

Compared with external-supervised learning-based (ESLB) methods, self-supervised
learning-based (SSLB) methods can overcome the domain gap problem caused by different …

Light field (LF) depth estimation is a crucial basis for LF-related applications. Most existing
methods are based on the Lambertian assumption and cannot deal with non-Lambertian …

Conventional and emerging paradigms of urbanism require new responses under the
current circumstances, especially in relation to the integration of sustainability dimensions …

Light field (LF) semantic segmentation is a newly arisen technology and is widely used in
many smart city applications such as remote sensing, virtual reality and 3D photogrammetry …

The principal purpose of this research is to utilize the exp-function approach for achieving
exact solutions of nonlinear optical fibers, including fractional order in the sense of …