One-shot Unsupervised Domain Adaptation with Personalized Diffusion Models - Télécom Paris Accéder directement au contenu
Communication Dans Un Congrès Année : 2023

One-shot Unsupervised Domain Adaptation with Personalized Diffusion Models

Résumé

Adapting a segmentation model from a labeled source domain to a target domain, where a single unlabeled datum is available, is one the most challenging problems in domain adaptation and is otherwise known as one-shot unsupervised domain adaptation (OSUDA). Most of the prior works have addressed the problem by relying on style transfer techniques, where the source images are stylized to have the appearance of the target domain. Departing from the common notion of transferring only the target ``texture'' information, we leverage text-to-image diffusion models (e.g., Stable Diffusion) to generate a synthetic target dataset with photo-realistic images that not only faithfully depict the style of the target domain, but are also characterized by novel scenes in diverse contexts. The text interface in our method Data AugmenTation with diffUsion Models (DATUM) endows us with the possibility of guiding the generation of images towards desired semantic concepts while respecting the original spatial context of a single training image, which is not possible in existing OSUDA methods. Extensive experiments on standard benchmarks show that our DATUM surpasses the state-of-the-art OSUDA methods by up to +7.1%. The implementation is available at https://github.com/yasserben/DATUM

Dates et versions

hal-04205024 , version 1 (12-09-2023)

Identifiants

Citer

Yasser Benigmim, Subhankar Roy, Slim Essid, Vicky Kalogeiton, Stéphane Lathuilière. One-shot Unsupervised Domain Adaptation with Personalized Diffusion Models. IEEE/CVF Conference on Computer Vision and Pattern Recognition- Workshop on Generative Models for Computer Vision, 2023, vancouver, Canada. ⟨hal-04205024⟩
45 Consultations
0 Téléchargements

Altmetric

Partager

Gmail Facebook X LinkedIn More