Diverse Intra- and Inter-Domain Activity Style Fusion for Cross-Person Generalization in Activity Recognition

• URL: http://arxiv.org/abs/2406.04609v2
• Date: Sat, 29 Jun 2024 03:15:51 GMT
• Title: Diverse Intra- and Inter-Domain Activity Style Fusion for Cross-Person Generalization in Activity Recognition
• Authors: Junru Zhang, Lang Feng, Zhidan Liu, Yuhan Wu, Yang He, Yabo Dong, Duanqing Xu,
• Abstract summary: Existing domain generalization methods often face challenges in capturing intra- and inter-domain style diversity. We propose a process conceptualized as domain padding to enrich the domain diversity. We introduce a style-fused sampling strategy to enhance data generation diversity. Our approach outperforms state-of-the-art DG methods in all human activity recognition tasks.
• Score: 8.850516669999292
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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