Gianluca Mancusi — accepted AI-conference papers

2025

DitHub: A Modular Framework for Incremental Open-Vocabulary Object Detection

Chiara Cappellino, Gianluca Mancusi, Matteo Mosconi, Angelo Porrello, Simone Calderara, Rita Cucchiara

NeurIPS 2025poster

Open-Vocabulary object detectors can generalize to an unrestricted set of categories through simple textual prompting. However, adapting these models to rare classes or reinforcing their abilities on multiple specialized domains remains essential. While recent methods rely on monolithic adaptation s…

Cited by 5 Source ↗Scholar ↗

2024

Is Multiple Object Tracking a Matter of Specialization?

Gianluca Mancusi, Mattia Bernardi, Aniello Panariello, Angelo Porrello, Rita Cucchiara, Simone Calderara

NeurIPS 2024poster

End-to-end transformer-based trackers have achieved remarkable performance on most human-related datasets. However, training these trackers in heterogeneous scenarios poses significant challenges, including negative interference - where the model learns conflicting scene-specific parameters - and li…

Cited by 0 Source ↗PDF ↗Scholar ↗

2023

TrackFlow: Multi-Object tracking with Normalizing Flows

Gianluca Mancusi, Aniello Panariello, Angelo Porrello, Matteo Fabbri, Simone Calderara, Rita Cucchiara

ICCV 2023poster

The field of multi-object tracking has recently seen a renewed interest in the good old schema of tracking-by-detection, as its simplicity and strong priors spare it from the complex design and painful babysitting of tracking-by-attention approaches. In view of this, we aim at extending tracking-by-…

Cited by 16 PDF ↗Scholar ↗