The Pattern Analysis and Computer Vision (PAVIS) research line is actively developing an artificial intelligence (AI) infrastructure to understand our physical space and the human behaviors within it.
The focus is on creating intelligent systems that jointly accomplish two main goals:
Pattern Analysis and Computer Vision
Spatial AI
Perceive and understand autonomously the real physical world, both static and dynamic, with its corresponding spatial semantic (where are things, what is their function, which is the dynamic of the scene).
Social AI
Perceive and understand humans and their behaviors as observed by cameras in relation to the physical spaces they are interacting with.
Relevant Papers
2023
Litrico M., Del Bue A., Morerio P.
Guiding Pseudo-labels with Uncertainty Estimation for Source-free Unsupervised Domain Adaptation
IEEE/CVF Conference on Computer Vision and Pattern Recognition
2023
Giuliari F., Skenderi G., Cristani M., Bue A.D., Wang Y.
Leveraging Commonsense for Object Localisation in Partial Scenes
IEEE Transactions on Pattern Analysis and Machine Intelligence
2023
Cavazza J., Murino V., Bue A.D.
No Adversaries to Zero-Shot Learning: Distilling an Ensemble of Gaussian Feature Generators
IEEE Transactions on Pattern Analysis and Machine Intelligence
2023
Ahmad S., Morerio P., Del Bue A.
Person Re-Identification without Identification via Event Anonymization
Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)
2023
Zohaib M., Del Bue A.
SC3K: Self-supervised and Coherent 3D Keypoints Estimation from Rotated, Noisy, and Decimated Point Cloud Data
Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)
Principal Investigator
Pattern Analysis and Computer Vision
Videos
Intelligent Sensor Network for assistive AI Research
Introduction to MEMEX Project
Aworld - AI powered visual localization
PAVIS - Overview
SceneUnderLight - IIT - UNIVR - OSRAM
Drone Localization with Augmented Acoustic Imaging