# OnPoint: Offline-to-Online Multi-Level Distillation for Point-Supervised Online Temporal Action Localization

arXiv: https://arxiv.org/abs/2607.00289 · HTML: https://mlsanity.com/paper/2607.00289/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 35 of its 54 references that are in the map.
- First posted on arXiv 1 Jul 2026.

## Builds on (most cited first, 30 of 35)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Gemini 2.5: Pushing the Frontier with Advanced Reasoning, Multimodality, Long Context, and Next Generation Agentic Capabilities](https://mlsanity.com/paper/2507.06261/) (2025)
- [Rescaling Egocentric Vision](https://mlsanity.com/paper/2006.13256/) (2020)
- [BMN: Boundary-Matching Network for Temporal Action Proposal Generation](https://mlsanity.com/paper/1907.09702/) (2019)
- [HOI4D: A 4D Egocentric Dataset for Category-Level Human-Object Interaction](https://mlsanity.com/paper/2203.01577/) (2022)
- [ActionFormer: Localizing Moments of Actions with Transformers](https://mlsanity.com/paper/2202.07925/) (2022)
- [BSN: Boundary Sensitive Network for Temporal Action Proposal Generation](https://mlsanity.com/paper/1806.02964/) (2018)
- [The THUMOS Challenge on Action Recognition for Videos "in the Wild"](https://mlsanity.com/paper/1604.06182/) (2016)
- [R-C3D: Region Convolutional 3D Network for Temporal Activity Detection](https://mlsanity.com/paper/1703.07814/) (2017)
- [Learning Salient Boundary Feature for Anchor-free Temporal Action Localization](https://mlsanity.com/paper/2103.13137/) (2021)
- [Temporal Context Aggregation Network for Temporal Action Proposal Refinement](https://mlsanity.com/paper/2103.13141/) (2021)
- [SF-Net: Single-Frame Supervision for Temporal Action Localization](https://mlsanity.com/paper/2003.06845/) (2020)
- [FineAction: A Fine-Grained Video Dataset for Temporal Action Localization](https://mlsanity.com/paper/2105.11107/) (2021)
- [Points as Queries: Weakly Semi-supervised Object Detection by Points](https://mlsanity.com/paper/2104.07434/) (2021)
- [ReAct: Temporal Action Detection with Relational Queries](https://mlsanity.com/paper/2207.07097/) (2022)
- [Learning Action Completeness from Points for Weakly-supervised Temporal Action Localization](https://mlsanity.com/paper/2108.05029/) (2021)
- [Memory-and-Anticipation Transformer for Online Action Understanding](https://mlsanity.com/paper/2308.07893/) (2023)
- [Action Sensitivity Learning for Temporal Action Localization](https://mlsanity.com/paper/2305.15701/) (2023)
- [A Survey on Content-Aware Video Analysis for Sports](https://mlsanity.com/paper/1703.01170/) (2017)
- [RCL: Recurrent Continuous Localization for Temporal Action Detection](https://mlsanity.com/paper/2203.07112/) (2022)
- [Learning to Refactor Action and Co-occurrence Features for Temporal Action Localization](https://mlsanity.com/paper/2206.11493/) (2022)
- [HR-Pro: Point-supervised Temporal Action Localization via Hierarchical Reliability Propagation](https://mlsanity.com/paper/2308.12608/) (2023)
- [Guided Online Distillation: Promoting Safe Reinforcement Learning by Offline Demonstration](https://mlsanity.com/paper/2309.09408/) (2023)
- [SlowFocus: Enhancing Fine-grained Temporal Understanding in Video LLM](https://mlsanity.com/paper/2602.03589/) (2026)
- [HAT: History-Augmented Anchor Transformer for Online Temporal Action Localization](https://mlsanity.com/paper/2408.06437/) (2024)
- [Online Temporal Action Localization with Memory-Augmented Transformer](https://mlsanity.com/paper/2408.02957/) (2024)
- [SimOn: A Simple Framework for Online Temporal Action Localization](https://mlsanity.com/paper/2211.04905/) (2022)
- [JOADAA: joint online action detection and action anticipation](https://mlsanity.com/paper/2309.06130/) (2023)
- [Offline-to-Online Knowledge Distillation for Video Instance Segmentation](https://mlsanity.com/paper/2302.07516/) (2023)
- [Understanding Contexts Inside Robot and Human Manipulation Tasks through a Vision-Language Model and Ontology System in a Video Stream](https://mlsanity.com/paper/2003.01163/) (2020)

## Built on by (most cited first, 1 of 1)

- [NBA_Streaming: A Large-Scale Benchmark for Fine-Grained Basketball Commentary Generation in Continuous Streams](https://mlsanity.com/paper/2608.09200/) (2026)

## Abstract (arXiv)

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.