Generative AIOpen SourceComputer Vision
GenerativeZoo
Unified repository of generative models for computer vision, providing reproducible baselines and tooling for the research community.
Open to collaborations on deployable generative imaging
Francisco Caetano
Generative imaging researcher building deployable models.
My research focuses on practical, deployable generative solutions for image generation and editing, domain adaptation, and out-of-distribution detection to tackle real-world problems in medical imaging. I am now extending this work into NLP, exploring autoregressive and diffusion-based language models.
Generative Imaging Domain Adaptation Out-of-Distribution Detection
Applied Research
Projects
Select initiatives that deliver generative modeling, domain adaptation, and OOD detection into practical computer vision tooling.
Peer-reviewed
Publications
Recent work exploring diffusion models, domain adaptation, and reliable out-of-distribution detection for medical imaging.
DisCoPatch: Taming Adversarially-driven Batch Statistics for Improved Out-of-Distribution Detection
Francisco Caetano, C. Viviers, L. Mondragon, P.H.N. de With, F. van der Sommen
ICCV 2025
MedShift: Implicit Conditional Transport for X-Ray Domain Adaptation
Francisco Caetano, C. Viviers, P.H.N. de With, F. van der Sommen
ICCV 2025 Workshop
MedSymmFlow: Bridging Generative Modeling and Classification in Medical Imaging through Symmetrical Flow Matching
Francisco Caetano, L. Abdi, C. Viviers, A. Valiuddin, F. van der Sommen
MICCAI 2025 Workshop
AdverX-Ray: Ensuring X-Ray Integrity Through Frequency-Sensitive Adversarial VAEs
Francisco Caetano, C. Viviers, L. Filatova, P.H.N. de With, F. van der Sommen
SPIE Medical Imaging 2025
Runner-up · Robert F. Wagner All-Conference Best Student Paper Award
Can Your Generative Model Detect Out-of-Distribution Covariate Shift?
C. Viviers, A. Valiuddin, Francisco Caetano, L. Abdi, L. Filatova, P.H.N. de With, F. van der Sommen
ECCV 2024 Workshop
Zero-Shot Image Anomaly Detection Using Generative Foundation Models
L. Abdi, A. Valiuddin, Francisco Caetano, C. Viviers, F. van der Sommen
ICCV 2025 Workshop
Out-of-Distribution Detection in Medical Imaging via Diffusion Trajectories
L. Abdi, Francisco Caetano, A. Valiuddin, C. Viviers, H. Joudeh, F. van der Sommen
MICCAI 2025 Workshop
Other publications & reviews
Diffusion-based Lung Nodule Synthesis for Advanced Evaluation of Deep Learning Models
C.H.B. Claessens, Francisco Caetano, K. van der Wulp, L.J.S. Ewals, F. van der Sommen
SPIE Medical Imaging 2025
Visual Data Processing for Anomaly Detection
Francisco Caetano
Master Thesis
Enhancing Weakly-Supervised Video Anomaly Detection with Temporal Constraints
Francisco Caetano, P. Carvalho, C. Mastralexi, J. Cardoso
IEEE Access
Unveiling the Performance of Video Anomaly Detection Models — a Benchmark-based Review
Francisco Caetano, P. Carvalho, J. Cardoso
Intelligent Systems with Applications
Deep Anomaly Detection for In-Vehicle Monitoring — An Application-Oriented Review
Francisco Caetano, P. Carvalho, J. Cardoso
Applied Sciences (MDPI)
Trajectory
Experience
Academic and industrial roles shaped by advancing generative modeling for medical imaging and safety-critical systems.
PhD Candidate
Eindhoven University of Technology
- Generative artificial intelligence for tailored synthetic image generation
- Supervised by dr.ir. Fons van der Sommen
Computer Vision Researcher
Fraunhofer Portugal AICOS
- Anomaly detection for in-line visual inspection
MSc Electrical Engineering
Faculty of Engineering, University of Porto
- Graduated with GPA of 18/20
- Published two journal articles during the Master thesis
Research Assistant
INESC TEC - CTM
- Handled occlusion-aware recognition of human activity
- Built real-time trajectory prediction for surrounding vehicles
BSc Electrical Engineering
Faculty of Engineering, University of Porto
- Graduated with GPA of 18/20
Contact
Let's collaborate on deployable generative solutions
I am always happy to collaborate on research, mentorship, or talks about generative modeling, robust machine learning, and medical imaging.