16 papers
Defending from GeoLocalization through Adversarial Road Trips
Niccolò Niccoli, Federico Becattini, Lorenzo Seidenari
Retrieval-based image geolocalization has emerged as a powerful technique for determining the location of a query image by matching it against a large, geotagged database. The succ…
PEPR: Privileged Event-based Predictive Regularization for Domain Generalization
Gabriele Magrini, Federico Becattini, Niccolò Biondi +1
Deep neural networks for visual perception are highly susceptible to domain shift, which poses a critical challenge for real-world deployment under conditions that differ from the…
Multiview Progress Prediction of Robot Activities
Elena Zoppellari, Federico Becattini, Marco Fiorucci +1
For robots to operate effectively and safely alongside humans, they must be able to understand the progress of ongoing actions. This ability, known as action progress prediction, i…
Immunizing Images from Text to Image Editing via Adversarial Cross-Attention
Matteo Trippodo, Federico Becattini, Lorenzo Seidenari
Recent advances in text-based image editing have enabled fine-grained manipulation of visual content guided by natural language. However, such methods are susceptible to adversaria…
Attacking Attention of Foundation Models Disrupts Downstream Tasks
Hondamunige Prasanna Silva, Federico Becattini, Lorenzo Seidenari
Foundation models represent the most prominent and recent paradigm shift in artificial intelligence. Foundation models are large models, trained on broad data that deliver high acc…
Drone Detection with Event Cameras
Gabriele Magrini, Lorenzo Berlincioni, Luca Cultrera +2
The diffusion of drones presents significant security and safety challenges. Traditional surveillance systems, particularly conventional frame-based cameras, struggle to reliably d…