Simulating Distribution Dynamics: Liquid Temporal Feature Evolution for Single-Domain Generalized Object Detection

Zihao Zhang; Yang Li; Aming Wu; Yahong Han

AAAI 2026technical0 citations

Simulating Distribution Dynamics: Liquid Temporal Feature Evolution for Single-Domain Generalized Object Detection

Zihao Zhang, Yang Li, Aming Wu, Yahong Han

Abstract

In this paper, we focus on Single-Domain Generalized Object Detection (Single-DGOD), aiming to transfer a detector trained on one source domain to multiple unknown domains. Existing methods for Single-DGOD typically rely on discrete data augmentation or static perturbation methods to expand data diversity, thereby mitigating the lack of access to target domain data. However, in real-world scenarios such as changes in weather or lighting conditions, domain shifts often occur continuously and gradually. Discrete augmentations and static perturbations fail to effectively capture the dynamic variation of feature distributions, thereby limiting the model

BibTeX

@inproceedings{aaai2026_simulatingdistri,
  title = {Simulating Distribution Dynamics: Liquid Temporal Feature Evolution for Single-Domain Generalized Object Detection},
  author = {Zihao Zhang and Yang Li and Aming Wu and Yahong Han},
  booktitle = {AAAI 2026},
  year = {2026}
}