Hands-off Image Editing: Language-guided Editing without any Task-specific Labeling, Masking or even Training

Rodrigo Santos, António Branco, João Ricardo Silva, Joao Rodrigues


Abstract
Instruction-guided image editing consists in taking an image and an instruction and delivering that image altered according to that instruction. State-of-the-art approaches to this task suffer from the typical scaling up and domain adaptation hindrances related to supervision as they eventually resort to some kind of task-specific labelling, masking or training. We propose a novel approach that does without any such task-specific supervision and offers thus a better potential for improvement. Its assessment demonstrates that it is highly effective, achieving very competitive performance.
Anthology ID:
2025.coling-main.640
Volume:
Proceedings of the 31st International Conference on Computational Linguistics
Month:
January
Year:
2025
Address:
Abu Dhabi, UAE
Editors:
Owen Rambow, Leo Wanner, Marianna Apidianaki, Hend Al-Khalifa, Barbara Di Eugenio, Steven Schockaert
Venue:
COLING
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
9546–9565
Language:
URL:
https://aclanthology.org/2025.coling-main.640/
DOI:
Bibkey:
Cite (ACL):
Rodrigo Santos, António Branco, João Ricardo Silva, and Joao Rodrigues. 2025. Hands-off Image Editing: Language-guided Editing without any Task-specific Labeling, Masking or even Training. In Proceedings of the 31st International Conference on Computational Linguistics, pages 9546–9565, Abu Dhabi, UAE. Association for Computational Linguistics.
Cite (Informal):
Hands-off Image Editing: Language-guided Editing without any Task-specific Labeling, Masking or even Training (Santos et al., COLING 2025)
Copy Citation:
PDF:
https://aclanthology.org/2025.coling-main.640.pdf