Catastrophic forgetting of previous knowledge is a critical issue in
con...
Neural implicit modeling permits to achieve impressive 3D reconstruction...
The development of computer vision algorithms for Unmanned Aerial Vehicl...
State-of-the-art multimodal semantic segmentation approaches combining L...
With the increasing availability of depth sensors, multimodal frameworks...
The standard class-incremental continual learning setting assumes a set ...
Most approaches for semantic segmentation use only information from colo...
Deep learning models dealing with image understanding in real-world sett...
Sparse active illumination enables precise time-of-flight depth sensing ...
Accurate scene understanding from multiple sensors mounted on cars is a ...
Monocular depth estimation is still an open challenge due to the ill-pos...
Deep neural networks are typically trained in a single shot for a specif...
Indirect Time-of-Flight cameras (iToF) are low-cost devices that provide...
Deep networks allow to obtain outstanding results in semantic segmentati...
Deep learning models achieve outstanding accuracy in semantic segmentati...
Deep convolutional neural networks for semantic segmentation allow to ac...
Deep neural networks suffer from the major limitation of catastrophic
fo...
Deep learning frameworks allowed for a remarkable advancement in semanti...
The semantic segmentation of parts of objects in the wild is a challengi...
The aim of this paper is to give an overview of the recent advancements ...
Unsupervised Domain Adaptation (UDA) aims at improving the generalizatio...
The supervised training of deep networks for semantic segmentation requi...
Although deep learning architectures have shown remarkable results in sc...
Deep learning techniques have been widely used in autonomous driving sys...
Deep learning architectures exhibit a critical drop of performance due t...