5 citations · 6 across the 9 of their papers we have counts for
14 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…
Prompt-based Consistent Video Colorization
Silvia Dani, Tiberio Uricchio, Lorenzo Seidenari
Existing video colorization methods struggle with temporal flickering or demand extensive manual input. We propose a novel approach automating high-fidelity video colorization usin…
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…
Depth-based Privileged Information for Boosting 3D Human Pose Estimation on RGB
Alessandro Simoni, Francesco Marchetti, Guido Borghi +6
Despite the recent advances in computer vision research, estimating the 3D human pose from single RGB images remains a challenging task, as multiple 3D poses can correspond to the…
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…