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Computer Engineer with a Passion for AI and Perception

Welcome to my GitHub! I'm a computer engineer with a strong passion for artificial intelligence, especially in the realm of perception applications

Current Projects

  • Description: This project aims to train a YOLOv7 to detect objects from UAV at low-flight conditions, exploiting different points of view during training.

  • Status: Done. Find the code and documentation in the repository.

  • Description: This project integrates prior knowledge into Scene Graph Generation using a neuro-symbolic approach based on Logit Tensor Network and Knowledge Graph Embedding
  • Status: Done. Find the code and documentation in the repository.
  • Description: This project builds an efficient framework to inference our UAV-based Thermal Imaging detector based on Rust using ROS 2.

  • Status: Done. Find the code and documentation in the repository.

  • Description: This project integrates prior knowledge into a transformer-based encoder for sentence matching.

  • Status: Training in progress, stay tuned for the final release!

Get in Touch

I'm open to collaboration and discussions. If you share my passion for AI, Autonomous Driving, please feel free to reach out:

Let's bring Autonomous Driving to the next level! 🚗🤖🌟

Paolo Dimasi's Projects

gcndepth icon gcndepth

Self-Supervised CNN-GCN Autoencoder for Monocular Depth Estimation

multiwoz icon multiwoz

Source code for end-to-end dialogue model from the MultiWOZ paper (Budzianowski et al. 2018, EMNLP)

pamasi icon pamasi

Config files for my GitHub profile.

phrase_matching_ltn icon phrase_matching_ltn

Neuro-symbolic model used to participate for the competition "U.S. Patent Phrase to Phrase Matching"

realtime_panoptic icon realtime_panoptic

Official PyTorch implementation of CVPR 2020 Oral: Real-Time Panoptic Segmentation from Dense Detections

ros_msft_onnx icon ros_msft_onnx

ONNX Runtime for the Robot Operating System (ROS), works on ROS1 and ROS2

sgg_av icon sgg_av

Scene Graph Generation in Autonomous Driving: a neuro-symbolic approach

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