1 citations · 2 across the 12 of their papers we have counts for
15 papers
A temporal graph dissimilarity measure capturing epidemic spreading behavior
Salvatore Freni, Lorenzo Dall'Amico, Francesca Pistilli +3
Comparing temporal networks is a challenging problem in network analysis. Commonly adopted methods compare structural properties of temporal graphs and can be highly sensitive to t…
GORDON: Graph-based Object-centric Rewards for Decomposition of Long-Horizon Manipulation
Andrea Protopapa, Davide Buoso, Francesca Pistilli +2
Learning long-horizon manipulation skills with reinforcement learning remains challenging due to the complexity of reward design, the limited guidance of sparse rewards, and the hi…
Learning to Evolve Scenes: Reasoning about Human Activities with Scene Graphs
Francesca Pistilli, Simone Alberto Peirone, Giuseppe Averta
Understanding human behavior while interacting with the surrounding world is crucial for many applications of embodied AI. First-person videos are particularly informative for this…
HiERO-StepG @ Ego4D Step Grounding Challenge: hierarchical activity understanding enables zero-shot step grounding
Andrea Zenotto, Simone Alberto Peirone, Francesca Pistilli +1
Procedural activities follow well-defined structures: whether we consider a cooking recipe or a mechanic repairing a car, these activities naturally decompose in a hierarchy of ste…
GAP: Geometric Anchor Pre-training for Data-Efficient Visuomotor Learning of Manipulation Tasks
Davide Buoso, Andrea Protopapa, Stefano Di Carlo +2
Learning visuomotor policies from scarce expert demonstrations remains a core challenge in robotic manipulation. A primary hurdle lies in distilling high-dimensional RGB representa…
Learning reusable concepts across different egocentric video understanding tasks
Simone Alberto Peirone, Francesca Pistilli, Antonio Alliegro +2
Our comprehension of video streams depicting human activities is naturally multifaceted: in just a few moments, we can grasp what is happening, identify the relevance and interacti…
HiERO: understanding the hierarchy of human behavior enhances reasoning on egocentric videos
Simone Alberto Peirone, Francesca Pistilli, Giuseppe Averta
Human activities are particularly complex and variable, and this makes challenging for deep learning models to reason about them. However, we note that such variability does have a…
FORESCENE: FOREcasting human activity via latent SCENE graphs diffusion
Antonio Alliegro, Francesca Pistilli, Tatiana Tommasi +1
Forecasting human-environment interactions in daily activities is challenging due to the high variability of human behavior. While predicting directly from videos is possible, it i…
Continual Learning Should Move Beyond Incremental Classification
Rupert Mitchell, Antonio Alliegro, Raffaello Camoriano +17
Continual learning (CL) is the sub-field of machine learning concerned with accumulating knowledge in dynamic environments. So far, CL research has mainly focused on incremental cl…
Hier-EgoPack: Hierarchical Egocentric Video Understanding with Diverse Task Perspectives
Simone Alberto Peirone, Francesca Pistilli, Antonio Alliegro +2
Our comprehension of video streams depicting human activities is naturally multifaceted: in just a few moments, we can grasp what is happening, identify the relevance and interacti…
A Backpack Full of Skills: Egocentric Video Understanding with Diverse Task Perspectives
Simone Alberto Peirone, Francesca Pistilli, Antonio Alliegro +1
Human comprehension of a video stream is naturally broad: in a few instants, we are able to understand what is happening, the relevance and relationship of objects, and forecast wh…
PEM: Prototype-based Efficient MaskFormer for Image Segmentation
Niccolò Cavagnero, Gabriele Rosi, Claudia Cuttano +4
Recent transformer-based architectures have shown impressive results in the field of image segmentation. Thanks to their flexibility, they obtain outstanding performance in multipl…
Graph learning in robotics: a survey
Francesca Pistilli, Giuseppe Averta
Deep neural networks for graphs have emerged as a powerful tool for learning on complex non-euclidean data, which is becoming increasingly common for a variety of different applica…
Entropic Score metric: Decoupling Topology and Size in Training-free NAS
Niccolò Cavagnero, Luca Robbiano, Francesca Pistilli +2
Neural Networks design is a complex and often daunting task, particularly for resource-constrained scenarios typical of mobile-sized models. Neural Architecture Search is a promisi…
Learning Graph-Convolutional Representations for Point Cloud Denoising
Francesca Pistilli, Giulia Fracastoro, Diego Valsesia +1
Point clouds are an increasingly relevant data type but they are often corrupted by noise. We propose a deep neural network based on graph-convolutional layers that can elegantly d…