Mohammad Fahes
CS PhD (Inria)
Research interests: Multimodal Learning, LLMs, Efficient Adaptation, Generalization, Computer Vision, Deep Learning
More broadly, I'm interested in machine learning, mathematics, epistemology, and science in general.
News
Publications
Domain Adaptation with a Single Vision-Language Embedding
IJCV 2026
A unified framework for domain adaptation that replaces all target data with a single vision-language latent vector.
CLIP's Visual Embedding Projector is a Few-shot Cornucopia
WACV 2026
Few-shot and test-time adaptation of VLMs need not be complicated: regularized fine-tuning of their projection matrix suffices.
FLOSS: Free Lunch in Open-vocabulary Semantic Segmentation
ICCV 2025
Improving open-vocabulary semantic segmentation with unsupervised prompt selection.
A Simple Recipe for Language-guided Domain Generalized Segmentation
CVPR 2024
Multi-source domain generalization can be replaced by a single source domain combined with random language prompts.
PØDA: Prompt-driven Zero-shot Domain Adaptation
ICCV 2023
A new framework for domain adaptation driven by a single natural-language prompt.
Unrolling PALM for Sparse Semi-blind Source Separation
ICLR 2022
An unrolled version of the PALM algorithm that reduces its sensitivity to initialization and improves source separation quality.