3 papers
cs.LG2026
FedSDAF: Leveraging Source Domain Awareness for Enhanced Federated Domain Generalization
Hongze Li, Zesheng Zhou, Zhenbiao Cao +3
Traditional Federated Domain Generalization (FedDG) methods focus on learning domain-invariant features or adapting to unseen target domains, often overlooking the unique knowledge…
cs.CV2025
Multi-Granularity Feature Calibration via VFM for Domain Generalized Semantic Segmentation
Xinhui Li, Xiaojie Guo
Domain Generalized Semantic Segmentation (DGSS) aims to improve the generalization ability of models across unseen domains without access to target data during training. Recent adv…
cs.CV2025
Set Pivot Learning: Redefining Generalized Segmentation with Vision Foundation Models
Xinhui Li, Xinyu He, Qiming Hu +1
In this paper, we introduce, for the first time, the concept of Set Pivot Learning, a paradigm shift that redefines domain generalization (DG) based on Vision Foundation Models (VF…