S
Simone Calderara
Researcher at University of Modena and Reggio Emilia
Publications - 152
Citations - 5621
Simone Calderara is an academic researcher from University of Modena and Reggio Emilia. The author has contributed to research in topics: Computer science & Cluster analysis. The author has an hindex of 28, co-authored 136 publications receiving 3925 citations. Previous affiliations of Simone Calderara include University of Teramo & University of Las Palmas de Gran Canaria.
Papers
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Journal ArticleDOI
Visual Tracking: An Experimental Survey
Arnold W. M. Smeulders,Dung M. Chu,Rita Cucchiara,Simone Calderara,Afshin Dehghan,Mubarak Shah +5 more
TL;DR: It is demonstrated that trackers can be evaluated objectively by survival curves, Kaplan Meier statistics, and Grubs testing, and it is found that in the evaluation practice the F-score is as effective as the object tracking accuracy (OTA) score.
Proceedings ArticleDOI
Latent Space Autoregression for Novelty Detection
TL;DR: In this article, a deep autoencoder with a parametric density estimator is used to learn the probability distribution underlying the latent representations with an autoregressive procedure, which effectively acts as a regularizer for the task at hand, by minimizing the differential entropy of the distribution spanned by latent vectors.
Proceedings Article
Dark Experience for General Continual Learning: a Strong, Simple Baseline
TL;DR: This work works towards General Continual Learning (GCL), where task boundaries blur and the domain and class distributions shift either gradually or suddenly, through Dark Experience Replay, namely matching the network's logits sampled throughout the optimization trajectory, thus promoting consistency with its past.
Posted Content
Latent Space Autoregression for Novelty Detection
TL;DR: This proposal designs a general unsupervised framework where a deep autoencoder is equipped with a parametric density estimator that learns the probability distribution underlying the latent representations with an autoregressive procedure and shows that a maximum likelihood objective effectively acts as a regularizer for the task at hand.
Journal ArticleDOI
Predicting the Driver's Focus of Attention: The DR(eye)VE Project
TL;DR: In this article, a model based on a multi-branch deep architecture was proposed to predict the driver's focus of attention while driving, which part of the scene around the vehicle is more critical for the task.