avatar

Pasquale De Marinis

Ph.D. Student
University of Bari Aldo Moro



About me

I am a Ph.D. in Computer Science and Mathematics at the Computational Intelligence Lab (CILAB), University of Bari Aldo Moro.

My research focuses on data-efficient and explainable deep learning for computer vision, with applications in large-scale and real-world visual understanding.

I am currently working on few-shot semantic segmentation and edge AI methods developed in collaboration with the Jheronimus Academy of Data Science (Netherlands).

Research interests

  • Computer Vision: Semantic segmentation, Scene understanding, Object detection
  • Machine Learning: Few-shot learning, Transfer learning, Explainable AI
  • Applied AI: Edge intelligence, Efficient deep architectures, Sustainable computing

News

February 2026 I defended my PhD thesis "Sustainable Computer Vision Techniques in Sustainable Domains", completing my PhD in Computer Science and Mathematics.
December 2025 New preprint on Few-Shot Segmentation: "DistillFSS: Synthesizing Few-Shot Knowledge into a Lightweight Segmentation Model" is now available: Webpage, arXiv, Code
November 2025 New preprint on Interpretable Few-Shot Segmentation: "Matching-Based Few-Shot Semantic Segmentation Models Are Interpretable by Design" is now available: Webpage, arXiv, Code
September 2025 I chaired the workshop "Advances in Drone Vision" at ICIAP 2025.
August 2025 We received the "Outstanding Paper Award" at IFSA-NAFIPS 2025 for our paper "Explainable Fuzzy GNNs for Leak Detection in Water Distribution Networks".
July 2025 Our Paper "Label Anything: Multi-Class Few-Shot Semantic Segmentation with Visual Prompts" has been accepted to ECAI 2025.
May 2025 We are organizing the workshop "International Workshop on Advances in Drone Vision" at ICIAP 2025.
September 2024 I started my visit at the Jheronimus Academy of Data Science (JADS) in 's-Hertogenbosch, Netherlands.
August 2024 Our Paper "RoWeeder: Unsupervised Weed Mapping through Crop-Row Detection" has been accepted to CVPPA at ECCV 2024.
October 2023 Our Paper "Weed mapping in multispectral drone imagery using lightweight vision transformers" has been accepted to Neurocomputing.
September 2023 I received the Best Distinction Paper Award at FEDCSIS 2023.
July 2023 I participated to DeepLearn Summer School 2023 in Gran Canaria, Spain.
April 2023 I participated as student volunteer to DeepLearn Spring 2023 in Bari, Italy.

Featured publications

  1. ECAI2025
    Label Anything: Multi-Class Few-Shot Semantic Segmentation with Visual Prompts
    Pasquale De Marinis*, Nicola Fanelli, Raffaele Scaringi, Emanuele Colonna, Giuseppe Fiameni, Gennaro Vessio, Giovanna Castellano
    European Conference on Artificial Intelligence (ECAI), 2025.
    PDF Code Project Page
    We introduce Label Anything, a transformer-based framework for multi-prompt, multi-way few-shot semantic segmentation. Our model unifies diverse visual prompts—points, boxes, and masks—into a single, flexible architecture that generalizes across tasks while reducing annotation effort. Evaluated on the COCO-20i benchmark, it achieves state-of-the-art performance and demonstrates strong multi-class segmentation capabilities.

  2. ECCVW2024
    RoWeeder: Unsupervised Weed Mapping through Crop-Row Detection
    Pasquale De Marinis*, Gennaro Vessio, Giovanna Castellano
    European Conference on Computer Vision Workshops (ECCVW), 2024.
    PDF Code Project Page
    We present RoWeeder, an unsupervised framework for weed mapping that integrates crop-row detection with a noise-resilient deep learning model. By generating pseudo-ground truth from crop-row geometry, our lightweight network learns to distinguish crops from weeds under noisy conditions. Evaluated on the WeedMap dataset, RoWeeder surpasses existing baselines and enables real-time, drone-based weed monitoring for precision agriculture.

  3. Neurocomputing
    Weed mapping in multispectral drone imagery using lightweight vision transformers
    Giovanna Castellano, Pasquale De Marinis*, Gennaro Vessio
    Neurocomputing
    PDF Code Project Page
    We introduce a lightweight Vision Transformer for drone-based multispectral weed mapping in precision agriculture. The model transfers knowledge from RGB pretraining to multispectral data, achieving state-of-the-art results on the WeedMap dataset and enabling efficient, non-invasive weed detection for targeted herbicide management.

  4. FEDCSIS
    Applying Knowledge Distillation to Improve Weed Mapping With Drones
    Giovanna Castellano, Pasquale De Marinis*, Gennaro Vessio
    18th Conference on Computer Science and Intelligence Systems
    PDF Code Project Page Best Distinction Paper Award
    Our approach employs state-of-the-art knowledge distillation to transfer performance from a large teacher network to a compact student model, achieving high accuracy on the WeedMap dataset with a fraction of the computation. The method enables efficient, drone-deployable weed detection for sustainable precision agriculture.


Powered by Jekyll and Minimal Light theme.