no code implementations • 19 Jun 2023 • Luca Franco, Paolo Mandica, Konstantinos Kallidromitis, Devin Guillory, Yu-Teng Li, Trevor Darrell, Fabio Galasso
In HALO (Hyperbolic Active Learning Optimization), for the first time, we propose the use of epistemic uncertainty as a data acquisition strategy, following the intuition of selecting data points that are the least known.
1 code implementation • 10 Mar 2023 • Luca Franco, Paolo Mandica, Bharti Munjal, Fabio Galasso
We propose to use hyperbolic uncertainty to determine the algorithmic learning pace, under the assumption that less uncertain samples should be more strongly driving the training, with a larger weight and pace.
Ranked #61 on Skeleton Based Action Recognition on NTU RGB+D 120
no code implementations • 24 Mar 2022 • Laura Laurenti, Elisa Tinti, Fabio Galasso, Luca Franco, Chris Marone
We demonstrate that DL models based on Long-Short Term Memory (LSTM) and Convolution Neural Networks predict labquakes under several conditions, and that fault zone stress can be predicted with fidelity, confirming that acoustic energy is a fingerprint of fault zone stress.
no code implementations • 22 Mar 2022 • Luca Franco, Leonardo Placidi, Francesco Giuliari, Irtiza Hasan, Marco Cristani, Fabio Galasso
This paper proposes the first in-depth study of Transformer Networks (TF) and Bidirectional Transformers (BERT) for the forecasting of the individual motion of people, without bells and whistles.
1 code implementation • ICCV 2021 • Theodoros Sofianos, Alessio Sampieri, Luca Franco, Fabio Galasso
For the first time, STS-GCN models the human pose dynamics only with a graph convolutional network (GCN), including the temporal evolution and the spatial joint interaction within a single-graph framework, which allows the cross-talk of motion and spatial correlations.
Ranked #1 on Human Pose Forecasting on 3DPW