{"ID":2840248,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2511.14945","arxiv_id":"2511.14945","title":"Unsupervised Discovery of Long-Term Spatiotemporal Periodic Workflows in Human Activities","abstract":"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. 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. Experiments show that: (i) our benchmark presents significant challenges to both unsupervised periodic detection methods and zero-shot approaches based on powerful large language models (LLMs); (ii) our baseline outperforms competing methods by a substantial margin in all evaluation tasks; and (iii) in real-world applications, our baseline demonstrates deployment advantages on par with traditional supervised workflow detection approaches, eliminating the need for annotation and retraining. Our project page is https://sites.google.com/view/periodicworkflow.","short_abstract":"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....","url_abs":"https://arxiv.org/abs/2511.14945","url_pdf":"https://arxiv.org/pdf/2511.14945v2","authors":"[\"Fan Yang\",\"Quanting Xie\",\"Atsunori Moteki\",\"Shoichi Masui\",\"Shan Jiang\",\"Kanji Uchino\",\"Yonatan Bisk\",\"Graham Neubig\"]","published":"2025-11-18T22:07:30Z","proceeding":"cs.CV","tasks":"[\"cs.CV\"]","methods":"[\"Large Language Model\",\"Language Model\"]","project_urls":"[\"https://sites.google.com/view/periodicworkflow\"]","has_code":false,"code_links":[{"ID":606953,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_id":2840248,"paper_url":"https://arxiv.org/abs/2511.14945","paper_title":"Unsupervised Discovery of Long-Term Spatiotemporal Periodic Workflows in Human Activities","repo_url":"https://github.com/google/safevalues","is_official":false,"mentioned_in_paper":false,"mentioned_in_github":true,"github_stars":0}]}