178 citations · 399 across the 17 of their papers we have counts for
21 papers
Adaptive Pseudo-Label Refinement by Negative Ensemble Learning for Source-Free Unsupervised Domain Adaptation
Waqar Ahmed, Pietro Morerio, Vittorio Murino
The majority of existing Unsupervised Domain Adaptation (UDA) methods presumes source and target domain data to be simultaneously available during training. Such an assumption may…
Single Image Human Proxemics Estimation for Visual Social Distancing
Maya Aghaei, Matteo Bustreo, Yiming Wang +3
In this work, we address the problem of estimating the so-called "Social Distancing" given a single uncalibrated image in unconstrained scenarios. Our approach proposes a semi-auto…
Complex-Object Visual Inspection via Multiple Lighting Configurations
Maya Aghaei, Matteo Bustreo, Pietro Morerio +3
The design of an automatic visual inspection system is usually performed in two stages. While the first stage consists in selecting the most suitable hardware setup for highlightin…
Intra-Camera Supervised Person Re-Identification
Xiangping Zhu, Xiatian Zhu, Minxian Li +3
Existing person re-identification (re-id) methods mostly exploit a large set of cross-camera identity labelled training data. This requires a tedious data collection and annotation…
Generative Pseudo-label Refinement for Unsupervised Domain Adaptation
Pietro Morerio, Riccardo Volpi, Ruggero Ragonesi +1
We investigate and characterize the inherent resilience of conditional Generative Adversarial Networks (cGANs) against noise in their conditioning labels, and exploit this fact in…
DMCL: Distillation Multiple Choice Learning for Multimodal Action Recognition
Nuno C. Garcia, Sarah Adel Bargal, Vitaly Ablavsky +3
In this work, we address the problem of learning an ensemble of specialist networks using multimodal data, while considering the realistic and challenging scenario of possible miss…
Unsupervised Domain-Adaptive Person Re-identification Based on Attributes
Xiangping Zhu, Pietro Morerio, Vittorio Murino
Pedestrian attributes, e.g., hair length, clothes type and color, locally describe the semantic appearance of a person. Training person re-identification (ReID) algorithms under th…
Audio-Visual Model Distillation Using Acoustic Images
Andrés F. Pérez, Valentina Sanguineti, Pietro Morerio +1
In this paper, we investigate how to learn rich and robust feature representations for audio classification from visual data and acoustic images, a novel audio data modality. Forme…
Learning with privileged information via adversarial discriminative modality distillation
Nuno C. Garcia, Pietro Morerio, Vittorio Murino
Heterogeneous data modalities can provide complementary cues for several tasks, usually leading to more robust algorithms and better performance. However, while training data can b…
Modality Distillation with Multiple Stream Networks for Action Recognition
Nuno Garcia, Pietro Morerio, Vittorio Murino
Diverse input data modalities can provide complementary cues for several tasks, usually leading to more robust algorithms and better performance. However, while a (training) datase…
Excitation Dropout: Encouraging Plasticity in Deep Neural Networks
Andrea Zunino, Sarah Adel Bargal, Pietro Morerio +3
We propose a guided dropout regularizer for deep networks based on the evidence of a network prediction defined as the firing of neurons in specific paths. In this work, we utilize…
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…
Adversarial Feature Augmentation for Unsupervised Domain Adaptation
Riccardo Volpi, Pietro Morerio, Silvio Savarese +1
Recent works showed that Generative Adversarial Networks (GANs) can be successfully applied in unsupervised domain adaptation, where, given a labeled source dataset and an unlabele…
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…
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…
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…
Correlation Alignment by Riemannian Metric for Domain Adaptation
Pietro Morerio, Vittorio Murino
Domain adaptation techniques address the problem of reducing the sensitivity of machine learning methods to the so-called domain shift, namely the difference between source (traini…
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…
Left/Right Hand Segmentation in Egocentric Videos
Alejandro Betancourt, Pietro Morerio, Emilia Barakova +3
Wearable cameras allow people to record their daily activities from a user-centered (First Person Vision) perspective. Due to their favorable location, wearable cameras frequently…
The Evolution of First Person Vision Methods: A Survey
Alejandro Betancourt, Pietro Morerio, Carlo S. Regazzoni +1
The emergence of new wearable technologies such as action cameras and smart-glasses has increased the interest of computer vision scientists in the First Person perspective. Nowada…