no code implementations • 21 Nov 2023 • Federico Rollo, Andrea Zunino, Gennaro Raiola, Fabio Amadio, Arash Ajoudani, Nikolaos Tsagarakis
Human-robot interaction (HRI) has become a crucial enabler in houses and industries for facilitating operational flexibility.
no code implementations • 30 Oct 2023 • Federico Rollo, Andrea Zunino, Nikolaos Tsagarakis, Enrico Mingo Hoffman, Arash Ajoudani
In today's Human-Robot Interaction (HRI) scenarios, a prevailing tendency exists to assume that the robot shall cooperate with the closest individual or that the scene involves merely a singular human actor.
no code implementations • 3 Jul 2023 • Federico Rollo, Gennaro Raiola, Andrea Zunino, Nikolaos Tsagarakis, Arash Ajoudani
To further explore this direction, we propose a framework that can autonomously detect and localize predefined objects in a known environment using a multi-modal sensor fusion approach (combining RGB and depth data from an RGB-D camera and a lidar).
no code implementations • 19 Apr 2021 • Waqar Ahmed, Andrea Zunino, Pietro Morerio, Vittorio Murino
The concept of compressing deep Convolutional Neural Networks (CNNs) is essential to use limited computation, power, and memory resources on embedded devices.
no code implementations • 13 Mar 2020 • Andrea Zunino, Sarah Adel Bargal, Riccardo Volpi, Mehrnoosh Sameki, Jianming Zhang, Stan Sclaroff, Vittorio Murino, Kate Saenko
Explanations are defined as regions of visual evidence upon which a deep classification network makes a decision.
no code implementations • 6 Dec 2018 • Sarah Adel Bargal, Andrea Zunino, Vitali Petsiuk, Jianming Zhang, Kate Saenko, Vittorio Murino, Stan Sclaroff
We propose Guided Zoom, an approach that utilizes spatial grounding of a model's decision to make more informed predictions.
1 code implementation • 23 May 2018 • Andrea Zunino, Sarah Adel Bargal, Pietro Morerio, Jianming Zhang, Stan Sclaroff, Vittorio Murino
In this work, we utilize the evidence at each neuron to determine the probability of dropout, rather than dropping out neurons uniformly at random as in standard dropout.
1 code implementation • CVPR 2018 • Sarah Adel Bargal, Andrea Zunino, Donghyun Kim, Jianming Zhang, Vittorio Murino, Stan Sclaroff
Models are trained to caption or classify activity in videos, but little is known about the evidence used to make such decisions.
no code implementations • 3 Aug 2017 • Andrea Zunino, Jacopo Cavazza, Atesh Koul, Andrea Cavallo, Cristina Becchio, Vittorio Murino
In this paper, we bridge cognitive and computer vision studies, by demonstrating the effectiveness of video-based approaches for the prediction of human intentions.
no code implementations • 31 May 2016 • Andrea Zunino, Jacopo Cavazza, Atesh Koul, Andrea Cavallo, Cristina Becchio, Vittorio Murino
In this paper, we address the new problem of the prediction of human intents.
no code implementations • 2 May 2016 • Andrea Zunino, Jacopo Cavazza, Vittorio Murino
In particular, considering that each human action in the datasets is performed several times by different subjects, we were able to precisely quantify the effect of inter- and intra-subject variability, so as to figure out the impact of several learning approaches in terms of classification performance.
no code implementations • 22 Apr 2016 • Jacopo Cavazza, Andrea Zunino, Marco San Biagio, Vittorio Murino
In this paper we aim at increasing the descriptive power of the covariance matrix, limited in capturing linear mutual dependencies between variables only.