1 citations · 2 across the 4 of their papers we have counts for
7 papers
Soft Teacher Forcing
Mohammad Fahes, Cordelia Schmid, Anurag Arnab
Soft tokens have so far been used to replace discrete chain-of-thought in latent reasoning methods. These approaches typically require multi-stage training and, in some cases, auxi…
FLOSS: Free Lunch in Open-vocabulary Semantic Segmentation
Yasser Benigmim, Mohammad Fahes, Tuan-Hung Vu +2
In this paper, we challenge the conventional practice in Open-Vocabulary Semantic Segmentation (OVSS) of using averaged class-wise text embeddings, which are typically obtained by…
Domain Adaptation with a Single Vision-Language Embedding
Mohammad Fahes, Tuan-Hung Vu, Andrei Bursuc +2
Domain adaptation has been extensively investigated in computer vision but still requires access to target data at the training time, which might be difficult to obtain in real-wor…
CLIP's Visual Embedding Projector is a Few-shot Cornucopia
Mohammad Fahes, Tuan-Hung Vu, Andrei Bursuc +2
We introduce ProLIP, a simple and architecture-agnostic method for adapting contrastively pretrained vision-language models, such as CLIP, to few-shot classification. ProLIP fine-t…
A Simple Recipe for Language-guided Domain Generalized Segmentation
Mohammad Fahes, Tuan-Hung Vu, Andrei Bursuc +2
Generalization to new domains not seen during training is one of the long-standing challenges in deploying neural networks in real-world applications. Existing generalization techn…
PØDA: Prompt-driven Zero-shot Domain Adaptation
Mohammad Fahes, Tuan-Hung Vu, Andrei Bursuc +2
Domain adaptation has been vastly investigated in computer vision but still requires access to target images at train time, which might be intractable in some uncommon conditions.…
Unrolling PALM for sparse semi-blind source separation
Mohammad Fahes, Christophe Kervazo, Jérôme Bobin +1
Sparse Blind Source Separation (BSS) has become a well established tool for a wide range of applications - for instance, in astrophysics and remote sensing. Classical sparse BSS me…