Overview
Machine unlearning has emerged as a crucial capability for computer vision models that must forget, remove, or steer away from undesired data or concepts. This workshop (in short, MUV) brings together researchers working on unlearning methods for recognition and generative tasks, including selective forgetting, safe image and video generation, privacy-preserving learning, and ethical compliance under regulations such as GDPR and CCPA.
By uniting technical advances with responsible AI practices, MUV aims to establish unlearning as a cornerstone of trustworthy visual intelligence. Forgetting is not the opposite of learning but its responsible counterpart.
Invited Speakers
Rohit Gandikota
Northeastern University
Sijia Liu
Michigan State University
...and more to be announced!
Schedule (Coming Soon)
Call for Papers
Topics Covered
MUV’s objective is to facilitate engaging and influential discussions across the following topics:
- Core methodologies for machine unlearning in vision, spanning data-centric approaches (curation, selective removal), parameter-centric strategies (fine-tuning), and training-free steering techniques (refusal vectors, prompt editing);
- Applications of unlearning in generative AI, with emphasis on safe image and video generation that avoids copyrighted, explicit, or biased content;
- Unlearning for recognition tasks such as face identification, medical imaging, or video classification, where privacy, fairness, and compliance are central;
- Ethical and legal implications of unlearning in vision, in light of privacy regulations and copyright compliance.
Submission Requirements
Important Notes:
- Submissions must use the CVPR 2026 Author Kit (LaTeX/Word).
- Follow all CVPR 2026 author instructions and submission policies.
- All submissions must be anonymized for double-blind review.
- Papers accepted to CVPR 2026 main conference may be submitted to the No Proceedings track.
- Submissions to another CVPR 2026 workshop are not permitted (for Proceedings Track).
Submission Tracks
Proceedings Track
Full papers (up to 8 pages). Accepted papers will be included in CVPR proceedings.
No Proceedings Track
Extended abstracts (up to 4 pages) or already published works (up to 8 pages). Ideal for preliminary results or stimulating discussion.
Organizers
Fabio Galasso
Sapienza University of Rome
Iacopo Masi
Sapienza University of Rome
Joanna Materzynska
Meta
Bardh Prenkaj
TU Munich
Alessio Sampieri
ItalAI
Bernt Schiele
MPI for Informatics