Diverse Intra- and Inter-Domain Activity Style Fusion for Cross-Person Generalization in Activity Recognition
<|reference_start|>Diverse Intra- and Inter-Domain Activity Style Fusion for Cross-Person Generalization in Activity Recognition: 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 broad range 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.<|reference_end|>
@article{zhang2024diverse,
    title={Diverse Intra- and Inter-Domain Activity Style Fusion for Cross-Person
  Generalization in Activity Recognition},
    author={Junru Zhang, Lang Feng, Zhidan Liu, Yuhan Wu, Yang He, Yabo Dong,
  Duanqing Xu},
    journal={arXiv preprint arXiv:2406.04609},
    year={2024},
    archivePrefix={arXiv},
    eprint={2406.04609},
    primaryClass={cs.LG cs.AI}
}