Existing domain generalization (DG) methods for cross-person generalization
tasks often face challenges in capturing intra- and inter-domain style
diversity, resulting in domain gaps with the target domain. In this study, we
explore a novel perspective to tackle this problem, a process conceptualized as
domain padding. This proposal aims to enrich the domain diversity by
synthesizing intra- and inter-domain style data while maintaining robustness to
class labels. We instantiate this concept using a conditional diffusion model
and introduce a style-fused sampling strategy to enhance data generation
diversity. In contrast to traditional condition-guided sampling, our
style-fused sampling strategy allows for the flexible use of one or more random
styles to guide data synthesis. This feature presents a notable advancement: it
allows for the maximum utilization of possible permutations and combinations
among existing styles to generate a broad spectrum of new style instances.
Empirical evaluations on a board of datasets demonstrate that our generated
data achieves remarkable diversity within the domain space. Both intra- and
inter-domain generated data have proven to be significant and valuable,
contributing to varying degrees of performance enhancements. Notably, our
approach outperforms state-of-the-art DG methods in all human activity
recognition tasks.

通过域填充的概念，使用条件扩散模型和样式融合采样策略生成多样的数据，解决了领域泛化中捕捉领域间和领域内样式多样性的挑战，并在所有人体活动识别任务中优于现有的领域泛化方法。