5 citations · 5 across the 5 of their papers we have counts for
5 papers
Addressing Limitations of State-Aware Imitation Learning for Autonomous Driving
Luca Cultrera, Federico Becattini, Lorenzo Seidenari +2
Conditional Imitation learning is a common and effective approach to train autonomous driving agents. However, two issues limit the full potential of this approach: (i) the inertia…
FLODCAST: Flow and Depth Forecasting via Multimodal Recurrent Architectures
Andrea Ciamarra, Federico Becattini, Lorenzo Seidenari +1
Forecasting motion and spatial positions of objects is of fundamental importance, especially in safety-critical settings such as autonomous driving. In this work, we address the is…
DiffDefense: Defending against Adversarial Attacks via Diffusion Models
Hondamunige Prasanna Silva, Lorenzo Seidenari, Alberto Del Bimbo
This paper presents a novel reconstruction method that leverages Diffusion Models to protect machine learning classifiers against adversarial attacks, all without requiring any mod…
3D Pose Nowcasting: Forecast the Future to Improve the Present
Alessandro Simoni, Francesco Marchetti, Guido Borghi +4
Technologies to enable safe and effective collaboration and coexistence between humans and robots have gained significant importance in the last few years. A critical component use…
Segmentation Free Object Discovery in Video
Giovanni Cuffaro, Federico Becattini, Claudio Baecchi +2
In this paper we present a simple yet effective approach to extend without supervision any object proposal from static images to videos. Unlike previous methods, these spatio-tempo…