Towards multimodal open-set domain generalization and adaptation through self-supervision
The task of open-set domain generalization (OSDG) involves recognizing novel classes
within unseen domains, which becomes more challenging with multiple modalities as input …
within unseen domains, which becomes more challenging with multiple modalities as input …
Unknown Prompt the only Lacuna: Unveiling CLIP's Potential for Open Domain Generalization
Abstract We delve into Open Domain Generalization (ODG) marked by domain and category
shifts between training's labeled source and testing's unlabeled target domains. Existing …
shifts between training's labeled source and testing's unlabeled target domains. Existing …
PracticalDG: Perturbation Distillation on Vision-Language Models for Hybrid Domain Generalization
Z Chen, W Wang, Z Zhao, F Su… - Proceedings of the …, 2024 - openaccess.thecvf.com
Abstract Domain Generalization (DG) aims to resolve distribution shifts between source and
target domains and current DG methods are default to the setting that data from source and …
target domains and current DG methods are default to the setting that data from source and …
SETA: Semantic-Aware Token Augmentation for Domain Generalization
Domain generalization (DG) aims to enhance the model robustness against domain shifts
without accessing target domains. A prevalent category of methods for DG is data …
without accessing target domains. A prevalent category of methods for DG is data …
The Devil Is in the Statistics: Mitigating and Exploiting Statistics Difference for Generalizable Semi-supervised Medical Image Segmentation
Despite the recent success of domain generalization in medical image segmentation, voxel-
wise annotation for all source domains remains a huge burden. Semi-supervised domain …
wise annotation for all source domains remains a huge burden. Semi-supervised domain …
Learn to Preserve and Diversify: Parameter-Efficient Group with Orthogonal Regularization for Domain Generalization
Abstract Domain generalization (DG) aims to avoid the performance degradation of the
model when the distribution shift between the limited training data and unseen test data …
model when the distribution shift between the limited training data and unseen test data …
Madod: Generalizing ood detection to unseen domains via g-invariance meta-learning
Real-world machine learning applications often face simultaneous covariate and semantic
shifts, challenging traditional domain generalization and out-of-distribution (OOD) detection …
shifts, challenging traditional domain generalization and out-of-distribution (OOD) detection …
Advancing Open-Set Domain Generalization Using Evidential Bi-Level Hardest Domain Scheduler
In Open-Set Domain Generalization (OSDG), the model is exposed to both new variations of
data appearance (domains) and open-set conditions, where both known and novel …
data appearance (domains) and open-set conditions, where both known and novel …
SETA: Semantic-Aware Edge-Guided Token Augmentation for Domain Generalization
Domain generalization (DG) aims to enhance the model robustness against domain shifts
without accessing target domains. A prevalent category of methods for DG is data …
without accessing target domains. A prevalent category of methods for DG is data …
A novel domain-private-suppress meta-recognition network based universal domain generalization for machinery fault diagnosis
M Xu, Y Zhang, B Lu, Z Liu, Q Sun - Knowledge-Based Systems, 2025 - Elsevier
Abstract Domain generalization aims to generalize knowledge to target domains not seen
during the training phase, even in domain gaps. However, in complex industrial settings, the …
during the training phase, even in domain gaps. However, in complex industrial settings, the …