# LAP-Net: Adaptive Features Sampling via Learning Action Progression for Online Action Detection

arXiv: https://arxiv.org/abs/2011.07915 · HTML: https://mlsanity.com/paper/2011.07915/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 16 Nov 2020.

## Built on by (most cited first, 13 of 13)

- [Long Short-Term Transformer for Online Action Detection](https://mlsanity.com/paper/2107.03377/) (2021)
- [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)
- [Weakly-Supervised Online Action Segmentation in Multi-View Instructional Videos](https://mlsanity.com/paper/2203.13309/) (2022)
- [A Survey on Deep Learning Techniques for Action Anticipation](https://mlsanity.com/paper/2309.17257/) (2023)
- [Human Action Anticipation: A Survey](https://mlsanity.com/paper/2410.14045/) (2024)
- [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)
- [MALT: Multi-scale Action Learning Transformer for Online Action Detection](https://mlsanity.com/paper/2405.20892/) (2024)
- [Multiview Progress Prediction of Robot Activities](https://mlsanity.com/paper/2603.00151/) (2026)
- [CAKE: Real-time Action Detection via Motion Distillation and Background-aware Contrastive Learning](https://mlsanity.com/paper/2603.23988/) (2026)

## Abstract (arXiv)

Online action detection is a task with the aim of identifying ongoing actions from streaming videos without any side information or access to future frames. Recent methods proposed to aggregate fixed temporal ranges of invisible but anticipated future frames representations as supplementary features and achieved promising performance. They are based on the observation that human beings often detect ongoing actions by contemplating the future vision simultaneously. However, we observed that at different action progressions, the optimal supplementary features should be obtained from distinct temporal ranges instead of simply fixed future temporal ranges. To this end, we introduce an adaptive features sampling strategy to overcome the mentioned variable-ranges of optimal supplementary features. Specifically, in this paper, we propose a novel Learning Action Progression Network termed LAP-Net, which integrates an adaptive features sampling strategy. At each time step, this sampling strategy first estimates current action progression and then decide what temporal ranges should be used to aggregate the optimal supplementary features. We evaluated our LAP-Net on three benchmark datasets, TVSeries, THUMOS-14 and HDD. The extensive experiments demonstrate that with our adaptive feature sampling strategy, the proposed LAP-Net can significantly outperform current state-of-the-art methods with a large margin.