Description: Mohammad Fahes, PhD student at Astra-vision team at Inria Paris, France.
I am a first-year PhD student at Astra-vision group, a joint team between Inria and valeo.ai . I'm currently working on scene understanding in adverse conditions, under the supervision of Raoul de Charette , Tuan-Hung Vu , Andrei Bursuc and Patrick Pérez . I received an MSc degree in Mathematics, Vision and Learning from ENS Paris-Saclay, an engineering degree from Mines Paris , and a mechanical engineering degree from Lebanese University .
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 conditions, especially for long-tail samples. In this paper, we propose the task of ‘Prompt-driven Zero-shot Domain Adaptation’, where we adapt a model trained on a source domain using only a general textual description of the target domain, i.e., a prompt. First, we leverage a pretrained contrastive vision-language model (CLIP) to optimize affine tra
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 methods, such as the Proximal Alternating Linearized Minimization (PALM) algorithm, nevertheless often suffer from a difficult hyperparameter choice, which undermines their results. To bypass this pitfall, we propose in this work to build on the thriving field of algorithm unfolding/unrolling. Unrolling PALM enables to leverage the