24 citations · 46 across the 18 of their papers we have counts for
22 papers
LiAM-SAM: Lifecycle-Aware Memory for Robust SAM2-Based MOT
Grégoire Francisco, Alessandro D'Amico, Samuele Costantini +2
Segmentation-based multi-object tracking (MOT) with foundation video models such as SAM2 offers strong localization quality, yet remains fragile in crowded, real-world scenes. In d…
Gaussian Processes for Modelling Spatial Fields with Robot Swarms
Guillermo Legarda Herranz, Gianpiero Francesca, Mauro Birattari
Robot swarms, by virtue of their decentralised architecture, are a natural tool for scalable, robust modelling of spatial fields, such as water temperature, wind velocity, or terra…
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…
Language-Based Swarm Perception: Decentralized Person Re-Identification via Natural Language Descriptions
Miquel Kegeleirs, Lorenzo Garattoni, Gianpiero Francesca +1
We introduce a method for decentralized person re-identification in robot swarms that leverages natural language as the primary representational modality. Unlike traditional approa…
Mixture of Experts Guided by Gaussian Splatters Matters: A new Approach to Weakly-Supervised Video Anomaly Detection
Giacomo D'Amicantonio, Snehashis Majhi, Quan Kong +4
Video Anomaly Detection (VAD) is a challenging task due to the variability of anomalous events and the limited availability of labeled data. Under the Weakly-Supervised VAD (WSVAD)…
Just Dance with ! A Poly-modal Inductor for Weakly-supervised Video Anomaly Detection
Snehashis Majhi, Giacomo D'Amicantonio, Antitza Dantcheva +5
Weakly-supervised methods for video anomaly detection (VAD) are conventionally based merely on RGB spatio-temporal features, which continues to limit their reliability in real-worl…