no code implementations • 18 Feb 2024 • Federico Becattini, Lorenzo Berlincioni, Luca Cultrera, Alberto del Bimbo
Neuromorphic sensors, also known as event cameras, are a class of imaging devices mimicking the function of biological visual systems.
no code implementations • 29 Jan 2024 • Lorenzo Berlincioni, Luca Cultrera, Federico Becattini, Alberto del Bimbo
Recognizing faces and their underlying emotions is an important aspect of biometrics.
1 code implementation • 14 Aug 2023 • Dario Cioni, Lorenzo Berlincioni, Federico Becattini, Alberto del Bimbo
Cultural heritage applications and advanced machine learning models are creating a fruitful synergy to provide effective and accessible ways of interacting with artworks.
no code implementations • 1 Jun 2023 • Lorenzo Berlincioni, Stefano Berretti, Marco Bertini, Alberto del Bimbo
Time varying sequences of 3D point clouds, or 4D point clouds, are now being acquired at an increasing pace in several applications (e. g., LiDAR in autonomous or assisted driving).
1 code implementation • 13 Apr 2023 • Lorenzo Berlincioni, Luca Cultrera, Chiara Albisani, Lisa Cresti, Andrea Leonardo, Sara Picchioni, Federico Becattini, Alberto del Bimbo
Recently, event cameras have shown large applicability in several computer vision fields especially concerning tasks that require high temporal resolution.
no code implementations • 25 Jun 2021 • Francesco Bongini, Lorenzo Berlincioni, Marco Bertini, Alberto del Bimbo
In this paper we propose a novel data augmentation approach for visual content domains that have scarce training datasets, compositing synthetic 3D objects within real scenes.
no code implementations • 3 Feb 2021 • My Kieu, Lorenzo Berlincioni, Leonardo Galteri, Marco Bertini, Andrew D. Bagdanov, Alberto del Bimbo
Experimental results demonstrate the effectiveness of our approach: using less than 50\% of available real thermal training data, and relying on synthesized data generated by our model in the domain adaptation phase, our detector achieves state-of-the-art results on the KAIST Multispectral Pedestrian Detection Benchmark; even if more real thermal data is available adding GAN generated images to the training data results in improved performance, thus showing that these images act as an effective form of data augmentation.
no code implementations • 18 Oct 2020 • Lorenzo Berlincioni, Federico Becattini, Lorenzo Seidenari, Alberto del Bimbo
Trajectory prediction is an important task, especially in autonomous driving.
no code implementations • 27 Aug 2020 • Claudio Ferrari, Lorenzo Berlincioni, Marco Bertini, Alberto del Bimbo
As additional contribution, we enrich the original dataset by using the annotated landmarks to deform and project the 3DMM onto the images.
no code implementations • 29 May 2018 • Lorenzo Berlincioni, Federico Becattini, Leonardo Galteri, Lorenzo Seidenari, Alberto del Bimbo
Autonomous driving is becoming a reality, yet vehicles still need to rely on complex sensor fusion to understand the scene they act in.