4 citations · 12 across the 11 of their papers we have counts for
12 papers
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…
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…
PolyDiff: Generating 3D Polygonal Meshes with Diffusion Models
Antonio Alliegro, Yawar Siddiqui, Tatiana Tommasi +1
We introduce PolyDiff, the first diffusion-based approach capable of directly generating realistic and diverse 3D polygonal meshes. In contrast to methods that use alternate 3D sha…
MeshGPT: Generating Triangle Meshes with Decoder-Only Transformers
Yawar Siddiqui, Antonio Alliegro, Alexey Artemov +5
We introduce MeshGPT, a new approach for generating triangle meshes that reflects the compactness typical of artist-created meshes, in contrast to dense triangle meshes extracted b…
OpenPatch: a 3D patchwork for Out-Of-Distribution detection
Paolo Rabino, Antonio Alliegro, Francesco Cappio Borlino +1
Moving deep learning models from the laboratory setting to the open world entails preparing them to handle unforeseen conditions. In several applications the occurrence of novel cl…
3DOS: Towards 3D Open Set Learning -- Benchmarking and Understanding Semantic Novelty Detection on Point Clouds
Antonio Alliegro, Francesco Cappio Borlino, Tatiana Tommasi
In recent years there has been significant progress in the field of 3D learning on classification, detection and segmentation problems. The vast majority of the existing studies fo…
End-to-End Learning to Grasp via Sampling from Object Point Clouds
Antonio Alliegro, Martin Rudorfer, Fabio Frattin +2
The ability to grasp objects is an essential skill that enables many robotic manipulation tasks. Recent works have studied point cloud-based methods for object grasping by starting…
Denoise and Contrast for Category Agnostic Shape Completion
Antonio Alliegro, Diego Valsesia, Giulia Fracastoro +2
In this paper, we present a deep learning model that exploits the power of self-supervision to perform 3D point cloud completion, estimating the missing part and a context region a…
Joint Supervised and Self-Supervised Learning for 3D Real-World Challenges
Antonio Alliegro, Davide Boscaini, Tatiana Tommasi
Point cloud processing and 3D shape understanding are very challenging tasks for which deep learning techniques have demonstrated great potentials. Still further progresses are ess…