# Learning Temporal Action Proposals With Fewer Labels

arXiv: https://arxiv.org/abs/1910.01286 · HTML: https://mlsanity.com/paper/1910.01286/

## Key facts

- Cited by 17 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 3 Oct 2019.

## Built on by (most cited first, 17 of 17)

- [Self-Supervised Learning for Semi-Supervised Temporal Action Proposal](https://mlsanity.com/paper/2104.03214/) (2021)
- [Deep Learning-based Action Detection in Untrimmed Videos: A Survey](https://mlsanity.com/paper/2110.00111/) (2021)
- [Augmented Transformer with Adaptive Graph for Temporal Action Proposal Generation](https://mlsanity.com/paper/2103.16024/) (2021)
- [FastAno: Fast Anomaly Detection via Spatio-temporal Patch Transformation](https://mlsanity.com/paper/2106.08613/) (2021)
- [Cyclical Self-Supervision for Semi-Supervised Ejection Fraction Prediction from Echocardiogram Videos](https://mlsanity.com/paper/2210.11291/) (2022)
- [Semi-Supervised Temporal Action Detection with Proposal-Free Masking](https://mlsanity.com/paper/2207.07059/) (2022)
- [Self-supervised Learning for Semi-supervised Temporal Language Grounding](https://mlsanity.com/paper/2109.11475/) (2021)
- [Weakly-guided Self-supervised Pretraining for Temporal Activity Detection](https://mlsanity.com/paper/2111.13675/) (2021)
- [Open-Vocabulary Action Localization with Iterative Visual Prompting](https://mlsanity.com/paper/2408.17422/) (2024)
- [Analytical Survey of Learning with Low-Resource Data: From Analysis to Investigation](https://mlsanity.com/paper/2510.08962/) (2025)
- [STAT: Towards Generalizable Temporal Action Localization](https://mlsanity.com/paper/2404.13311/) (2024)
- [A Survey of Learning on Small Data: Generalization, Optimization, and Challenge](https://mlsanity.com/paper/2207.14443/) (2022)
- [Boosting Semi-Supervised Temporal Action Localization by Learning from Non-Target Classes](https://mlsanity.com/paper/2403.11189/) (2024)
- [Towards Adaptive Pseudo-label Learning for Semi-Supervised Temporal Action Localization](https://mlsanity.com/paper/2407.07673/) (2024)
- [Workflow Augmentation of Video Data for Event Recognition with Time-Sensitive Neural Networks](https://mlsanity.com/paper/2109.15063/) (2021)
- [Enabling Weakly-Supervised Temporal Action Localization from On-Device Learning of the Video Stream](https://mlsanity.com/paper/2208.12673/) (2022)
- [Active Learning with Effective Scoring Functions for Semi-Supervised Temporal Action Localization](https://mlsanity.com/paper/2208.14856/) (2022)

## Abstract (arXiv)

Temporal action proposals are a common module in action detection pipelines today. Most current methods for training action proposal modules rely on fully supervised approaches that require large amounts of annotated temporal action intervals in long video sequences. The large cost and effort in annotation that this entails motivate us to study the problem of training proposal modules with less supervision. In this work, we propose a semi-supervised learning algorithm specifically designed for training temporal action proposal networks. When only a small number of labels are available, our semi-supervised method generates significantly better proposals than the fully-supervised counterpart and other strong semi-supervised baselines. We validate our method on two challenging action detection video datasets, ActivityNet v1.3 and THUMOS14. We show that our semi-supervised approach consistently matches or outperforms the fully supervised state-of-the-art approaches.