The goal of semantic segmentation is to segment the input image according to semantic
information and predict the semantic category of each pixel from a given label set. With the …

Semantic segmentation has a broad range of applications in a variety of domains including
land coverage analysis, autonomous driving, and medical image analysis. Convolutional …

Abstract Models trained in federated settings often suffer from degraded performances and
fail at generalizing, especially when facing heterogeneous scenarios. In this work, we …

In recent years visual place recognition (VPR), ie, the problem of recognizing the location of
images, has received considerable attention from multiple research communities, spanning …

Machine learning (ML) is widely used for key tasks in Connected and Automated Vehicles
(CAV), including perception, planning, and control. However, its reliance on vehicular data …

The development of computer vision algorithms for Unmanned Aerial Vehicles (UAVs)
imagery heavily relies on the availability of annotated high-resolution aerial data. However …

Autonomous driving techniques have been flourishing in recent years while thirsting for
huge amounts of high-quality data. However, it is difficult for real-world datasets to keep up …

Autonomous vehicles (AVs) are emerging in the automobile industry with potential benefits
to reduce traffic congestion, improve mobility and accessibility, as well as safety. According …

Although existing semantic segmentation approaches achieve impressive results, they still
struggle to update their models incrementally as new categories are uncovered …

Connected and Automated Vehicles (CAVs) represent a rapidly growing technology in the
automotive domain sector, offering promising solutions to address challenges such as traffic …