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Hi! I'm Alessandro Flaborea

Phd in Computer Science @ Sapienza University of Rome

  • šŸ”­ Research areas: Computer Vision and Machine Learning
  • šŸŒ± Iā€™m currently working on Video Anomaly Detection, Human Pose Forecasting, Egocentric Vision, Hyperbolic Neural Networks
  • šŸ§‘ā€šŸ’» I'm a member of the Perception and Intelligence Lab (PINlab)
  • šŸ“– You can find my Publications here: Google Scholar
  • šŸ“« How to reach me: Linkedin Badge Gmail Badge X
  • āš” Tools I daily use: Python PyTorch VS Code Weights & Biases

Alessandro Flaborea's Projects

bestpractices2body icon bestpractices2body

The official PyTorch implementation of the 5th IEEE/CVF CVPR Precognition Workshop paper Best Practices for 2-Body Pose Forecasting.

hypad icon hypad

The official PyTorch implementation of the IEEE/CVF CVPR Visual Anomaly and Novelty Detection (VAND) Workshop paper Are we certain it's anomalous?.

mocodad icon mocodad

The official PyTorch implementation of the IEEE/CVF International Conference on Computer Vision (ICCV) '23 paper Multimodal Motion Conditioned Diffusion Model for Skeleton-based Video Anomaly Detection.

paperbadger icon paperbadger

Issuing badges to credit authors for their work on academic papers

prego icon prego

The official PyTorch implementation of the IEEE/CVF Computer Vision and Pattern Recognition (CVPR) '24 paper PREGO: online mistake detection in PRocedural EGOcentric videos.

stats-under-the-stars-2019 icon stats-under-the-stars-2019

Partecipation at SUS2019 Hackathon in Milan at the Bocconi University. Awarded as the team which best performed using SAS software and platform.

street-classification icon street-classification

Statistical Learning project. The team followed all the processes, from the data collection to the data preprocessing and the classification.

wikipedia-hyperlinks-analysis icon wikipedia-hyperlinks-analysis

The goal of this project is to perform an analysis of the Wikipedia Hyperlink graph and, in particular, to rank the articles within the categories according to some criteria and using NetworkX.

yellow-taxis-anlysis-in-ny-city icon yellow-taxis-anlysis-in-ny-city

This analysis tries to point out some interresting informations about all the rides made in the first semester of 2018. The work concentrats both in the whole NY city and in the single zones.

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