Summary of Diverse Intra- and Inter-domain Activity Style Fusion For Cross-person Generalization in Activity Recognition, by Junru Zhang et al.
Diverse Intra- and Inter-Domain Activity Style Fusion for Cross-Person Generalization in Activity Recognition
by Junru Zhang, Lang Feng, Zhidan Liu, Yuhan Wu, Yang He, Yabo Dong, Duanqing Xu
First submitted to arxiv on: 7 Jun 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper proposes a novel approach called domain padding to tackle the challenge of cross-person generalization tasks in machine learning. Existing methods often struggle to capture intra- and inter-domain style diversity, resulting in domain gaps with the target domain. The authors introduce a conditional diffusion model that synthesizes style data while maintaining robustness to class labels. They also propose a style-fused sampling strategy that allows for flexible use of random styles to generate new style instances. This approach enables the maximum utilization of possible permutations and combinations among existing styles, resulting in a broad spectrum of new style instances. Empirical evaluations on various datasets demonstrate that the generated data achieves remarkable diversity within the domain space. The authors’ approach outperforms state-of-the-art domain generalization methods in human activity recognition tasks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This research paper is about making machines better at recognizing human activities, like walking or running. Right now, machines are good at recognizing things they’ve seen before, but they struggle to recognize new and different styles of the same activity. The authors propose a new way to solve this problem by creating fake data that looks like it could be real. They use a special computer program to generate this data, which helps the machine learn to recognize activities in a more flexible way. This means the machine can better understand activities it hasn’t seen before, like someone walking with a cane or riding a bike. The authors tested their approach on many different datasets and found that it worked really well. |
Keywords
» Artificial intelligence » Activity recognition » Diffusion model » Domain generalization » Generalization » Machine learning
