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Perception and Intelligence Laboratory (PINlab)

Department of Computer Science

Sapienza University of Rome, Italy

Computer vision and machine learning have great potential to endow machines/robots with the (visual) perception of the environment and the intelligence to reason about it, and take decisions. The field has thrived in the past three decades, and it stands now as one of the key technological ingredients for autonomous driving, unmanned drones, and human-robot-collaboration, as well as the pervasive novel asset for other fields of science, including earthquake and weather forecasting.

Our lab is interested in fundamental research and innovation transfer on computer vision and machine learning. Our specific research interests include distributed and multi-agent intelligent systems, perception (detection, recognition, re-identification, forecasting), and general intelligence (reasoning, meta-learning, domain adaptation), within sustainable (low-power-consumption and constrained-computational-resource sensors and devices) and interpretable (interpretable and verifiable AI) frameworks.

RECENT PUBLICATIONS

[All Publications]
  1. Muhammad Rameez Ur Rahman,  Jhony H. Giraldo,  Indro Spinelli,  Stéphane Lathuilière,  and Fabio Galasso
    OVOSE: Open-Vocabulary Semantic Segmentation in Event-Based Cameras
    In International Conference on Pattern Recognition (ICPR), 2024
  2. Paolo Mandica,  Luca Franco,  Konstantinos Kallidromitis,  Suzanne Petryk,  and Fabio Galasso
    Hyperbolic Learning with Multimodal Large Language Models
    In European Conference on Computer Vision (ECCV) workshops, 2024
  3. Alessio Sampieri,  Alessio Palma,  Indro Spinelli,  and Fabio Galasso
    Length-Aware Motion Synthesis via Latent Diffusion
    In European Conference on Computer Vision (ECCV), 2024
  4. Guido Maria D’Amely Di Melendugno,  Alessandro Flaborea,  Pascal Mettes,  and Fabio Galasso
    Hyp2Nav: Hyperbolic Planning and Curiosity for Crowd Navigation
    IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2024
  5. Massimiliano Pappa,  Luca Collorone,  Giovanni Ficarra,  Indro Spinelli,  and Fabio Galasso
    MoDiPO: text-to-motion alignment via AI-feedback-driven Direct Preference Optimization
    arXiv preprint arXiv:2404.11327, 2024