38 citations · 42 across the 4 of their papers we have counts for
5 papers
Generalising via Meta-Examples for Continual Learning in the Wild
Alessia Bertugli, Stefano Vincenzi, Simone Calderara +1
Future deep learning systems call for techniques that can deal with the evolving nature of temporal data and scarcity of annotations when new problems occur. As a step towards this…
Few-Shot Unsupervised Continual Learning through Meta-Examples
Alessia Bertugli, Stefano Vincenzi, Simone Calderara +1
In real-world applications, data do not reflect the ones commonly used for neural networks training, since they are usually few, unlabeled and can be available as a stream. Hence m…
DAG-Net: Double Attentive Graph Neural Network for Trajectory Forecasting
Alessio Monti, Alessia Bertugli, Simone Calderara +1
Understanding human motion behaviour is a critical task for several possible applications like self-driving cars or social robots, and in general for all those settings where an au…
AC-VRNN: Attentive Conditional-VRNN for Multi-Future Trajectory Prediction
Alessia Bertugli, Simone Calderara, Pasquale Coscia +2
Anticipating human motion in crowded scenarios is essential for developing intelligent transportation systems, social-aware robots and advanced video surveillance applications. A k…
Learning to Grasp from 2.5D images: a Deep Reinforcement Learning Approach
Alessia Bertugli, Paolo Galeone
In this paper, we propose a deep reinforcement learning (DRL) solution to the grasping problem using 2.5D images as the only source of information. In particular, we developed a si…