# WOAD: Weakly Supervised Online Action Detection in Untrimmed Videos

arXiv: https://arxiv.org/abs/2006.03732 · HTML: https://mlsanity.com/paper/2006.03732/

## Key facts

- Cited by 19 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 5 Jun 2020.

## Built on by (most cited first, 19 of 19)

- [Long Short-Term Transformer for Online Action Detection](https://mlsanity.com/paper/2107.03377/) (2021)
- [Memory-and-Anticipation Transformer for Online Action Understanding](https://mlsanity.com/paper/2308.07893/) (2023)
- [Deep Learning-based Action Detection in Untrimmed Videos: A Survey](https://mlsanity.com/paper/2110.00111/) (2021)
- [GateHUB: Gated History Unit with Background Suppression for Online Action Detection](https://mlsanity.com/paper/2206.04668/) (2022)
- [Stochastic Backpropagation: A Memory Efficient Strategy for Training Video Models](https://mlsanity.com/paper/2203.16755/) (2022)
- [Context-Enhanced Memory-Refined Transformer for Online Action Detection](https://mlsanity.com/paper/2503.18359/) (2025)
- [Weakly-Supervised Online Action Segmentation in Multi-View Instructional Videos](https://mlsanity.com/paper/2203.13309/) (2022)
- [ENIGMA-51: Towards a Fine-Grained Understanding of Human-Object Interactions in Industrial Scenarios](https://mlsanity.com/paper/2309.14809/) (2023)
- [E2E-LOAD: End-to-End Long-form Online Action Detection](https://mlsanity.com/paper/2306.07703/) (2023)
- [JOADAA: joint online action detection and action anticipation](https://mlsanity.com/paper/2309.06130/) (2023)
- [A Circular Window-based Cascade Transformer for Online Action Detection](https://mlsanity.com/paper/2208.14209/) (2022)
- [ActionSwitch: Class-agnostic Detection of Simultaneous Actions in Streaming Videos](https://mlsanity.com/paper/2407.12987/) (2024)
- [Weakly Supervised Online Action Detection for Infant General Movements](https://mlsanity.com/paper/2208.03648/) (2022)
- [MALT: Multi-scale Action Learning Transformer for Online Action Detection](https://mlsanity.com/paper/2405.20892/) (2024)
- [Pose-Aware Weakly-Supervised Action Segmentation](https://mlsanity.com/paper/2504.05700/) (2025)
- [StreamSense: Streaming Social Task Detection with Selective Vision-Language Model Routing](https://mlsanity.com/paper/2601.22738/) (2026)
- [MistExit: Learning to Exit for Early Mistake Detection in Procedural Videos](https://mlsanity.com/paper/2603.14252/) (2026)
- [Text-driven Online Action Detection](https://mlsanity.com/paper/2501.13518/) (2025)
- [Mamba-OTR: a Mamba-based Solution for Online Take and Release Detection from Untrimmed Egocentric Video](https://mlsanity.com/paper/2507.16342/) (2025)

## Abstract (arXiv)

Online action detection in untrimmed videos aims to identify an action as it happens, which makes it very important for real-time applications. Previous methods rely on tedious annotations of temporal action boundaries for training, which hinders the scalability of online action detection systems. We propose WOAD, a weakly supervised framework that can be trained using only video-class labels. WOAD contains two jointly-trained modules, i.e., temporal proposal generator (TPG) and online action recognizer (OAR). Supervised by video-class labels, TPG works offline and targets at accurately mining pseudo frame-level labels for OAR. With the supervisory signals from TPG, OAR learns to conduct action detection in an online fashion. Experimental results on THUMOS'14, ActivityNet1.2 and ActivityNet1.3 show that our weakly-supervised method largely outperforms weakly-supervised baselines and achieves comparable performance to the previous strongly-supervised methods. Beyond that, WOAD is flexible to leverage strong supervision when it is available. When strongly supervised, our method obtains the state-of-the-art results in the tasks of both online per-frame action recognition and online detection of action start.