316 citations · 347 across the 11 of their papers we have counts for
10 papers · 1 filter
Stochastic Coherence Over Attention Trajectory For Continuous Learning In Video Streams
Matteo Tiezzi, Simone Marullo, Lapo Faggi +3
Devising intelligent agents able to live in an environment and learn by observing the surroundings is a longstanding goal of Artificial Intelligence. From a bare Machine Learning p…
Evaluating Continual Learning Algorithms by Generating 3D Virtual Environments
Enrico Meloni, Alessandro Betti, Lapo Faggi +3
Continual learning refers to the ability of humans and animals to incrementally learn over time in a given environment. Trying to simulate this learning process in machines is a ch…
Gravitational Models Explain Shifts on Human Visual Attention
Dario Zanca, Marco Gori, Stefano Melacci +1
Visual attention refers to the human brain's ability to select relevant sensory information for preferential processing, improving performance in visual and cognitive tasks. It pro…
Toward Improving the Evaluation of Visual Attention Models: a Crowdsourcing Approach
Dario Zanca, Stefano Melacci, Marco Gori
Human visual attention is a complex phenomenon. A computational modeling of this phenomenon must take into account where people look in order to evaluate which are the salient loca…
Video Surveillance of Highway Traffic Events by Deep Learning Architectures
Matteo Tiezzi, Stefano Melacci, Marco Maggini +1
In this paper we describe a video surveillance system able to detect traffic events in videos acquired by fixed videocameras on highways. The events of interest consist in a specif…
Learning Visual Features Under Motion Invariance
Alessandro Betti, Marco Gori, Stefano Melacci
Humans are continuously exposed to a stream of visual data with a natural temporal structure. However, most successful computer vision algorithms work at image level, completely di…