activity
20172021
most citedMinimal-Entropy Correlation Alignment for Unsupervised Deep Domain Adaptation

41 citations · 101 across the 10 of their papers we have counts for

collaborators

17 papers

cs.CV2021

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…

cs.CV20204 cited

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…

cs.CV2020

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…

cs.CV2020

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…

cs.CV2020

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…

cs.CV201910 cited

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…