activity
20202026
most citedSceneAdapt: Scene-based domain adaptation for semantic segmentation using adversarial learning

20 citations · 20 across the 4 of their papers we have counts for

collaborators

5 papers

cs.CV2026

ENIGMA-360: An Ego-Exo Dataset for Human Behavior Understanding in Industrial Scenarios

Francesco Ragusa, Rosario Leonardi, Michele Mazzamuto +6

Understanding human behavior from complementary egocentric (ego) and exocentric (exo) points of view enables the development of systems that can support workers in industrial envir…

cs.CV2026

ProSkill: Segment-Level Skill Assessment in Procedural Videos

Michele Mazzamuto, Daniele Di Mauro, Gianpiero Francesca +2

Skill assessment in procedural videos is crucial for the objective evaluation of human performance in settings such as manufacturing and procedural daily tasks. Current research on…

cs.CV2023

Synchronization is All You Need: Exocentric-to-Egocentric Transfer for Temporal Action Segmentation with Unlabeled Synchronized Video Pairs

Camillo Quattrocchi, Antonino Furnari, Daniele Di Mauro +2

We consider the problem of transferring a temporal action segmentation system initially designed for exocentric (fixed) cameras to an egocentric scenario, where wearable cameras ca…

cs.CV2022

Panoptic Segmentation using Synthetic and Real Data

Camillo Quattrocchi, Daniele Di Mauro, Antonino Furnari +1

Being able to understand the relations between the user and the surrounding environment is instrumental to assist users in a worksite. For instance, understanding which objects a u…

cs.CV202020 cited

SceneAdapt: Scene-based domain adaptation for semantic segmentation using adversarial learning

Daniele Di Mauro, Antonino Furnari, Giuseppe Patanè +2

Semantic segmentation methods have achieved outstanding performance thanks to deep learning. Nevertheless, when such algorithms are deployed to new contexts not seen during trainin…