no code implementations • 31 Oct 2023 • Luca Cultrera, Federico Becattini, Lorenzo Seidenari, Pietro Pala, Alberto del Bimbo
We feed the state of the vehicle along with the representation of the environment as a special token of the transformer and propagate it throughout the network.
no code implementations • 31 Oct 2023 • Andrea Ciamarra, Federico Becattini, Lorenzo Seidenari, Alberto del Bimbo
We train the proposed model to also perform predictions for several timesteps in the future.
no code implementations • 31 Oct 2023 • Andrea Ciamarra, Roberto Caldelli, Federico Becattini, Lorenzo Seidenari, Alberto del Bimbo
In particular, when an image (video) is captured the overall geometry of the scene (e. g. surfaces) and the acquisition process (e. g. illumination) determine a univocal environment that is directly represented by the image pixel values; all these intrinsic relations are possibly changed by the deepfake generation process.
1 code implementation • 7 Sep 2023 • 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 modifications to the classifiers themselves.
no code implementations • 24 Aug 2023 • Alessandro Simoni, Francesco Marchetti, Guido Borghi, Federico Becattini, Lorenzo Seidenari, Roberto Vezzani, Alberto del Bimbo
Technologies to enable safe and effective collaboration and coexistence between humans and robots have gained significant importance in the last few years.
no code implementations • 15 Nov 2022 • Andrea Ciamarra, Federico Becattini, Lorenzo Seidenari, Alberto del Bimbo
For an autonomous vehicle it is essential to observe the ongoing dynamics of a scene and consequently predict imminent future scenarios to ensure safety to itself and others.
no code implementations • 7 Jun 2022 • Alessandra Alfani, Federico Becattini, Lorenzo Seidenari, Alberto del Bimbo
Several unsupervised and self-supervised approaches have been developed in recent years to learn visual features from large-scale unlabeled datasets.
no code implementations • 23 Mar 2022 • Francesco Marchetti, Federico Becattini, Lorenzo Seidenari, Alberto del Bimbo
Effective modeling of human interactions is of utmost importance when forecasting behaviors such as future trajectories.
Ranked #5 on Trajectory Prediction on Stanford Drone
1 code implementation • 14 May 2021 • Fabio Zappardino, Tiberio Uricchio, Lorenzo Seidenari, Alberto del Bimbo
To understand human behavior we must not just recognize individual actions but model possibly complex group activity and interactions.
Ranked #8 on Group Activity Recognition on Volleyball
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 • 5 Jun 2020 • Luca Cultrera, Lorenzo Seidenari, Federico Becattini, Pietro Pala, Alberto del Bimbo
Current deep learning based autonomous driving approaches yield impressive results also leading to in-production deployment in certain controlled scenarios.
1 code implementation • CVPR 2020 • Francesco Marchetti, Federico Becattini, Lorenzo Seidenari, Alberto del Bimbo
Autonomous vehicles are expected to drive in complex scenarios with several independent non cooperating agents.
no code implementations • 9 Oct 2019 • Marco Menardi, Alex Falcon, Saida S. Mohamed, Lorenzo Seidenari, Giuseppe Serra, Alberto del Bimbo, Carlo Tasso
To address this issue, in this paper we propose an approach capable of generating images starting from a given text using conditional GANs trained on uncaptioned images dataset.
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.
1 code implementation • 4 May 2017 • Federico Becattini, Tiberio Uricchio, Lorenzo Seidenari, Lamberto Ballan, Alberto del Bimbo
In this paper we deal with the problem of predicting action progress in videos.
no code implementations • ICCV 2017 • Leonardo Galteri, Lorenzo Seidenari, Marco Bertini, Alberto del Bimbo
Moreover we show that our approach can be used as a pre-processing step for object detection in case images are degraded by compression to a point that state-of-the art detectors fail.
no code implementations • 1 Sep 2016 • Giovanni Cuffaro, Federico Becattini, Claudio Baecchi, Lorenzo Seidenari, Alberto del Bimbo
In this paper we present a simple yet effective approach to extend without supervision any object proposal from static images to videos.
no code implementations • 16 May 2016 • Tiberio Uricchio, Lamberto Ballan, Lorenzo Seidenari, Alberto del Bimbo
Automatic image annotation is among the fundamental problems in computer vision and pattern recognition, and it is becoming increasingly important in order to develop algorithms that are able to search and browse large-scale image collections.