  Single-domain generalization aims to learn a model from single source domain
data to achieve generalized performance on other unseen target domains.
Existing works primarily focus on improving the generalization ability of
static networks. However, static networks are unable to dynamically adapt to
the diverse variations in different image scenes, leading to limited
generalization capability. Different scenes exhibit varying levels of
complexity, and the complexity of images further varies significantly in
cross-domain scenarios. In this paper, we propose a dynamic object-centric
perception network based on prompt learning, aiming to adapt to the variations
in image complexity. Specifically, we propose an object-centric gating module
based on prompt learning to focus attention on the object-centric features
guided by the various scene prompts. Then, with the object-centric gating
masks, the dynamic selective module dynamically selects highly correlated
feature regions in both spatial and channel dimensions enabling the model to
adaptively perceive object-centric relevant features, thereby enhancing the
generalization capability. Extensive experiments were conducted on
single-domain generalization tasks in image classification and object
detection. The experimental results demonstrate that our approach outperforms
state-of-the-art methods, which validates the effectiveness and generally of
our proposed method.
