whoami

Hey there! I'm Francesco Pelosin, I'm Italian and I'm from Bassano del Grappa (north-east). I recently got a Ph.D. (july 2022) from Ca' Foscari University of Venice where I focused my studies on Continual Learning for Computer Vision. If you need to contact me, drop an e-mail here: pelosinf[at]gmail. Uh oh...lastly: I still consider myself an script k1ddie :D

My path of studies/work 🎓

resources

publications

• 

  Dynamic Label Injection for Imbalanced Industrial Defect Segmentation

  Emanuele Caruso*, Francesco Pelosin*, Alessandro Simoni*, Marco Boschetti

  VISION Workshop (ECCV 2024)

  💻 code | 📄 pdf | 📌 cite

  @inproceedings{caruso2024dynamic,
              title={Dynamic Label Injection for Imbalanced Industrial Defect Segmentation},
              author={Caruso, Emanuele and Pelosin, Francesco and Simoni, Alessandro and Boschetti, Marco},
              booktitle={Proceedings of the European Conference on Computer Vision Workshops},
              year={2024}
            }

• 

  DUCK: Distance-based Unlearning via Centroid Kinematics

  Marco Cotogni*, Jacopo Bonato*, Luigi Sabetta*, Francesco Pelosin, Alessandro Nicolosi

  ... submitted ... (WACV 2025)

  💻 code | 📄 pdf | 📌 cite

  @misc{cotogni2023duck,
              title={DUCK: Distance-based Unlearning via Centroid Kinematics}, 
              author={Marco Cotogni and Jacopo Bonato and Luigi Sabetta and Francesco Pelosin and Alessandro Nicolosi},
              year={2023},
              eprint={2312.02052},
              archivePrefix={arXiv},
              primaryClass={cs.CV}
              }

• 

  MIND: Multi-Task Incremental Network Distillation

  Jacopo Bonato*, Francesco Pelosin*, Luigi Sabetta*, Alessandro Nicolosi

  AAAI (2024)

  💻 code | 📄 pdf | 📌 cite

  @misc{mind2024,
              author={Bonato, Jacopo and Pelosin, Francesco and Sabetta, Luigi and Nicolosi, Alessandro},
              title = {MIND: Multi-Task Incremental Network Distillation},
              publisher = AAAI Conference on Artificial Intelligence (AAAI 2024),
              year = {2024},
              }

• 

  Simpler Is Better: off-the-shelf Continual Learning Through Pretrained Backbones

  Francesco Pelosin

  Workshop T4V (CVPR 2022)

  💻 code | 📄 pdf | 📌 cite

  @misc{pelosin2022simpler,
              author = {Pelosin, Francesco},
              title = {Simpler is Better: off-the-shelf Continual Learning Through Pretrained Backbones},
              publisher = Transformers 4 Vision Workshop (CVPR 2022),
              year = {2022},
              }

• 

  Towards Exemplar-Free Continual Learning in Vision Transformers: an Account of Attention, Functional and Weight Regularization

  Francesco Pelosin*, Saurav Jha*, Andrea Torsello, Bogdan Raducanu, Joost van de Weijer

  Workshop CLVISION (CVPR 2022) - Runner-Up Best Paper Award

  💻 code | 📄 pdf | 📌 cite

  @inproceedings{pelosin2022towards,
              title={Towards exemplar-free continual learning in vision transformers: an account of attention, functional and weight regularization},
              author={Pelosin, Francesco and Jha, Saurav and Torsello, Andrea and Raducanu, Bogdan and van de Weijer, Joost},
              booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
              booksubtitle={Workshop on Continual Learning},
              pages={3820--3829},
              year={2022}
          }

• 

  Smaller is Better: An Analysis of Instance Quantity/Quality Trade-off in Rehearsal-based Continual Learning

  Francesco Pelosin, Andrea Torsello

  IJCNN (2022)

  💻 code | 📄 pdf | 📌 cite

  @misc{pelosin2021better,
              title={Smaller Is Better: An Analysis of Instance Quantity/Quality Trade-off in Rehearsal-based Continual Learning}, 
              author={Francesco Pelosin and Andrea Torsello},
              year={2022},
              book={IJCNN}
          }
          

• 

  Unsupervised semantic discovery through visual patterns detection

  Francesco Pelosin, Andrea Gasparetto, Andrea Albarelli, Andrea Torsello

  S+SSPR (2022)

  💻 code | 📄 pdf | 📌 cite

  @article{DBLP:journals/corr/abs-2102-12213,
                  author    = {Francesco Pelosin and
                                  Andrea Gasparetto and
                                  Andrea Albarelli and
                                  Andrea Torsello},
                  title     = {Unsupervised semantic discovery through visual patterns detection},
                  journal   = {S+SSPR},
                  year      = {2021},
                  }
          

• 

  Separating Structure from Noise in Large Graphs Using the Regularity Lemma

  Marco Fiorucci, Francesco Pelosin, Marcello Pelillo

  Pattern Recognition (2020)

  💻 code | 📄 pdf | 📌 cite

  @article{FIORUCCI2020107070,
              title = {Separating Structure from Noise in Large Graphs Using the Regularity Lemma},
              journal = {Pattern Recognition},
              volume = {98},
              pages = {107070},
              year = {2020},
              issn = {0031-3203},
              doi = {https://doi.org/10.1016/j.patcog.2019.107070},
              url = {https://www.sciencedirect.com/science/article/pii/S0031320319303711},
              author = {Marco Fiorucci and Francesco Pelosin and Marcello Pelillo},
              keywords = {Regularity lemma, Graph summarization, Structural patterns, Noise, Randomness, Graph similarity search},
              abstract = {How can we separate structural information from noise in large graphs? To address this fundamental question, we propose a graph summarization approach based on Szemerédi’s Regularity Lemma, a well-known result in graph theory, which roughly states that every graph can be approximated by the union of a small number of random-like bipartite graphs called “regular pairs”. Hence, the Regularity Lemma provides us with a principled way to describe the essential structure of large graphs using a small amount of data. Our paper has several contributions: (i) We present our summarization algorithm which is able to reveal the main structural patterns in large graphs. (ii) We discuss how to use our summarization framework to efficiently retrieve from a database the top-k graphs that are most similar to a query graph. (iii) Finally, we evaluate the noise robustness of our approach in terms of the reconstruction error and the usefulness of the summaries in addressing the graph search task.}
              }
          
          

• 

  Graph Compression Using The Regularity Method

  Francesco Pelosin

  arxiv (2019) M.Sc. Thesis

  💻 code | 📄 pdf | 📌 cite

  @article{DBLP:journals/corr/abs-1810-07275,
                  author    = {Francesco Pelosin},
                  title     = {Graph Compression Using The Regularity Method},
                  journal   = {CoRR},
                  volume    = {abs/1810.07275},
                  year      = {2018},
                  url       = {http://arxiv.org/abs/1810.07275},
                  eprinttype = {arXiv},
                  eprint    = {1810.07275},
                  timestamp = {Tue, 30 Oct 2018 20:39:56 +0100},
                  biburl    = {https://dblp.org/rec/journals/corr/abs-1810-07275.bib},
                  bibsource = {dblp computer science bibliography, https://dblp.org}
                  }
                        
              
          

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