Pasquale De Marinis | 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

July 2026 Our paper "Take a Peek: Efficient Encoder Adaptation for Few-Shot Semantic Segmentation via LoRA" has been accepted to Pattern Recognition Letters: Webpage, arXiv, Code

May 2026 I spent a one-month research visit at CiTIUS - Centro Singular de Investigación en Tecnoloxías Intelixentes, University of Santiago de Compostela, Spain, collaborating with José María Alonso Moral on the explainability of Vision-Language Models.

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

PRL

Take a Peek: Efficient Encoder Adaptation for Few-Shot Semantic Segmentation via LoRA

Pasquale De Marinis*, Gennaro Vessio, Giovanna Castellano

Pattern Recognition Letters, 2026.

PDF Code Project Page

Few-shot semantic segmentation (FSS) aims to segment novel classes in query images using only a small annotated support set. We present TaP, which enhances encoder adaptability for both FSS and cross-domain FSS by inducing a lightweight feature-space shift conditioned on the support set. Leveraging Low-Rank Adaptation, TaP enables efficient encoder fine-tuning with minimal computational cost while achieving strong segmentation performance.

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.

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.

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.