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--- |
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title: LSPW Periodic Workflow Discovery |
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emoji: 🔄 |
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colorFrom: blue |
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colorTo: indigo |
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sdk: gradio |
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sdk_version: "6.2.0" |
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app_file: app.py |
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pinned: false |
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--- |
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# Unsupervised Discovery of Long-Term Spatiotemporal Periodic Workflows in Human Activities |
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[Project Page](https://sites.google.com/view/periodicworkflow) | [arXiv](https://www.arxiv.org/abs/2511.14945) |
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## Abstract |
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Periodic human activities with implicit workflows are common in manufacturing, sports, and daily life. While short-term periodic activities—characterized by simple structures and high-contrast patterns—have been widely studied, long-term periodic workflows with low-contrast patterns remain largely underexplored. |
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To bridge this gap, we introduce the first benchmark comprising 580 multimodal human activity sequences featuring long-term periodic workflows. The benchmark supports three evaluation tasks aligned with real-world applications: unsupervised periodic workflow detection, task completion tracking, and procedural anomaly detection. We also propose a lightweight, training-free baseline for modeling diverse periodic workflow patterns. |
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## Usage |
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### Dependencies |
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Ensure you have the following Python packages installed: |
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- `numpy` |
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- `scikit-learn` |
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- `tqdm` |
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- `matplotlib` |
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- `scipy` |
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You can install them using pip: |
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```bash |
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pip install numpy scikit-learn tqdm matplotlib scipy |
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``` |
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### Estimation |
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Run the workflow detection function to perform unsupervised periodic workflow detection on the dataset. |
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