{"ID":5552886,"CreatedAt":"2026-07-02T01:54:51.863792489Z","UpdatedAt":"2026-07-04T00:22:55.87949448Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2607.00289","arxiv_id":"2607.00289","title":"OnPoint: Offline-to-Online Multi-Level Distillation for Point-Supervised Online Temporal Action Localization","abstract":"Temporal Action Localization (TAL) typically relies on segment annotations or offline access to full videos, limiting scalability and online use. We introduce Point-Supervised Online TAL (POTAL), which localizes actions in streaming videos using only one temporal point per instance. To solve POTAL, we propose OnPoint, an offline-to-online multi-level distillation framework that transfers knowledge from a point-supervised offline teacher to an online student via (i) pseudo-segment instance distillation, (ii) class-activation sequence distillation, and (iii) anticipatory window-level distillation. We further improve robustness by incorporating the original point labels into student training and by refining anchor decoding with actionness-guided attention calibration. Experiments on five datasets show OnPoint consistently outperforms strong baselines, establishing a solid foundation for POTAL.","short_abstract":"Temporal Action Localization (TAL) typically relies on segment annotations or offline access to full videos, limiting scalability and online use. We introduce Point-Supervised Online TAL (POTAL), which localizes actions in streaming videos using only one temporal point per instance. To solve POTAL, we propose OnPoint,...","url_abs":"https://arxiv.org/abs/2607.00289","url_pdf":"https://arxiv.org/pdf/2607.00289v1","authors":"[\"Sakib Reza\",\"Gauri Jagatap\",\"Mohsen Moghaddam\",\"Octavia Camps\",\"Andrea Fanelli\"]","published":"2026-07-01T00:32:04Z","proceeding":"cs.CV","tasks":"[\"cs.CV\"]","methods":"[]","has_code":false}