Semantic segmentation is a challenging task that needs to handle large scale variations, deformations and different viewpoints. In this paper, we develop novel network named Gated Path Selection Network (GPSNet), which aims learn adaptive receptive fields. GPSNet, first design two-dimensional multi-scale - SuperNet, densely incorporates features from growing To dynamically select desirable sema...

Engineers often need to discover and learn designs from unfamiliar domains for inspiration or other particular uses. However, the complexity of technical design descriptions unfamiliarity domain make it hard engineers comprehend function, behavior, structure a design. To help quickly understand complex description new them, one approach is represent as network graph design-related entities thei...

Increasing migration is a vital concern for a globalizing sociocultural environment in today’s world. The UK and developed European countries have become an attractive destination for asylum seekers (labelled as “migrants”) in the past decade. The rapid rise in the number of asylum seekers, which was labelled “migration crisis” (Ruz, 2015), made this topic an integral part of scientific discuss...

This paper focuses on the high-resolution (HR) remote sensing images semantic segmentation task, whose goal is to predict labels in a pixel-wise manner. Due rich complexity and heterogeneity of information HR images, ability extract spatial details (boundary information) context dominates performance segmentation. In this paper, based frequently used fully convolutional network framework, we pr...

Social and information networks undermine the real relationship between individuals (ego) friends (alters) they are connected with on social media. The structure of individual network is highlighted by ego network. Egocentric approach popular due to its focus individuals, groups, or communities. Size, structure, composition directly impact networks. Moreover, analysis includes strength – alter ...