127 citations · 420 across the 42 of their papers we have counts for
8 papers · 1 filter
Video action detection by learning graph-based spatio-temporal interactions
Matteo Tomei, Lorenzo Baraldi, Simone Calderara +2
Action Detection is a complex task that aims to detect and classify human actions in video clips. Typically, it has been addressed by processing fine-grained features extracted fro…
Spotting insects from satellites: modeling the presence of Culicoides imicola through Deep CNNs
Stefano Vincenzi, Angelo Porrello, Pietro Buzzega +6
Nowadays, Vector-Borne Diseases (VBDs) raise a severe threat for public health, accounting for a considerable amount of human illnesses. Recently, several surveillance plans have b…
Warp and Learn: Novel Views Generation for Vehicles and Other Objects
Andrea Palazzi, Luca Bergamini, Simone Calderara +1
In this work we introduce a new self-supervised, semi-parametric approach for synthesizing novel views of a vehicle starting from a single monocular image. Differently from paramet…
A Deep Learning based approach to VM behavior identification in cloud systems
Matteo Stefanini, Riccardo Lancellotti, Lorenzo Baraldi +1
Cloud computing data centers are growing in size and complexity to the point where monitoring and management of the infrastructure become a challenge due to scalability issues. A p…
Multi-views Embedding for Cattle Re-identification
Luca Bergamini, Angelo Porrello, Andrea Capobianco Dondona +4
People re-identification task has seen enormous improvements in the latest years, mainly due to the development of better image features extraction from deep Convolutional Neural N…
Classifying Signals on Irregular Domains via Convolutional Cluster Pooling
Angelo Porrello, Davide Abati, Simone Calderara +1
We present a novel and hierarchical approach for supervised classification of signals spanning over a fixed graph, reflecting shared properties of the dataset. To this end, we intr…
Domain Translation with Conditional GANs: from Depth to RGB Face-to-Face
Matteo Fabbri, Guido Borghi, Fabio Lanzi +3
Can faces acquired by low-cost depth sensors be useful to catch some characteristic details of the face? Typically the answer is no. However, new deep architectures can generate RG…
Can Adversarial Networks Hallucinate Occluded People With a Plausible Aspect?
Federico Fulgeri, Matteo Fabbri, Stefano Alletto +2
When you see a person in a crowd, occluded by other persons, you miss visual information that can be used to recognize, re-identify or simply classify him or her. You can imagine i…