41 citations · 109 across the 18 of their papers we have counts for
8 papers · 1 filter
Scalable and Compact 3D Action Recognition with Approximated RBF Kernel Machines
Jacopo Cavazza, Pietro Morerio, Vittorio Murino
Despite the recent deep learning (DL) revolution, kernel machines still remain powerful methods for action recognition. DL has brought the use of large datasets and this is typical…
Minimal-Entropy Correlation Alignment for Unsupervised Deep Domain Adaptation
Pietro Morerio, Jacopo Cavazza, Vittorio Murino
In this work, we face the problem of unsupervised domain adaptation with a novel deep learning approach which leverages on our finding that entropy minimization is induced by the o…
Dropout as a Low-Rank Regularizer for Matrix Factorization
Jacopo Cavazza, Pietro Morerio, Benjamin Haeffele +3
Regularization for matrix factorization (MF) and approximation problems has been carried out in many different ways. Due to its popularity in deep learning, dropout has been applie…
An Analysis of Dropout for Matrix Factorization
Jacopo Cavazza, Connor Lane, Benjamin D. Haeffele +2
Dropout is a simple yet effective algorithm for regularizing neural networks by randomly dropping out units through Bernoulli multiplicative noise, and for some restricted problem…
A Compact Kernel Approximation for 3D Action Recognition
Jacopo Cavazza, Pietro Morerio, Vittorio Murino
3D action recognition was shown to benefit from a covariance representation of the input data (joint 3D positions). A kernel machine feed with such feature is an effective paradigm…
What Will I Do Next? The Intention from Motion Experiment
Andrea Zunino, Jacopo Cavazza, Atesh Koul +3
In computer vision, video-based approaches have been widely explored for the early classification and the prediction of actions or activities. However, it remains unclear whether t…
When Kernel Methods meet Feature Learning: Log-Covariance Network for Action Recognition from Skeletal Data
Jacopo Cavazza, Pietro Morerio, Vittorio Murino
Human action recognition from skeletal data is a hot research topic and important in many open domain applications of computer vision, thanks to recently introduced 3D sensors. In…
Curriculum Dropout
Pietro Morerio, Jacopo Cavazza, Riccardo Volpi +2
Dropout is a very effective way of regularizing neural networks. Stochastically "dropping out" units with a certain probability discourages over-specific co-adaptations of feature…