# HR-Pro: Point-supervised Temporal Action Localization via Hierarchical Reliability Propagation

arXiv: https://arxiv.org/abs/2308.12608 · HTML: https://mlsanity.com/paper/2308.12608/

## Key facts

- Cited by 16 papers in the map (data through 14 Oct 2026).
- Builds on 25 of its 39 references that are in the map.
- First posted on arXiv 24 Aug 2023.

## Builds on (most cited first, 25 of 25)

- [Quo Vadis, Action Recognition? A New Model and the Kinetics Dataset](https://mlsanity.com/paper/1705.07750/) (2017)
- [Soft-NMS -- Improving Object Detection With One Line of Code](https://mlsanity.com/paper/1704.04503/) (2017)
- [BMN: Boundary-Matching Network for Temporal Action Proposal Generation](https://mlsanity.com/paper/1907.09702/) (2019)
- [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)
- [G-TAD: Sub-Graph Localization for Temporal Action Detection](https://mlsanity.com/paper/1911.11462/) (2019)
- [The THUMOS Challenge on Action Recognition for Videos "in the Wild"](https://mlsanity.com/paper/1604.06182/) (2016)
- [UntrimmedNets for Weakly Supervised Action Recognition and Detection](https://mlsanity.com/paper/1703.03329/) (2017)
- [Temporal Context Aggregation Network for Temporal Action Proposal Refinement](https://mlsanity.com/paper/2103.13141/) (2021)
- [AutoLoc: Weakly-supervised Temporal Action Localization](https://mlsanity.com/paper/1807.08333/) (2018)
- [Background Suppression Network for Weakly-supervised Temporal Action Localization](https://mlsanity.com/paper/1911.09963/) (2019)
- [CoLA: Weakly-Supervised Temporal Action Localization with Snippet Contrastive Learning](https://mlsanity.com/paper/2103.16392/) (2021)
- [SF-Net: Single-Frame Supervision for Temporal Action Localization](https://mlsanity.com/paper/2003.06845/) (2020)
- [Hybrid Relation Guided Set Matching for Few-shot Action Recognition](https://mlsanity.com/paper/2204.13423/) (2022)
- [Learning Action Completeness from Points for Weakly-supervised Temporal Action Localization](https://mlsanity.com/paper/2108.05029/) (2021)
- [ASM-Loc: Action-aware Segment Modeling for Weakly-Supervised Temporal Action Localization](https://mlsanity.com/paper/2203.15187/) (2022)
- [MoLo: Motion-augmented Long-short Contrastive Learning for Few-shot Action Recognition](https://mlsanity.com/paper/2304.00946/) (2023)
- [Reliable Propagation-Correction Modulation for Video Object Segmentation](https://mlsanity.com/paper/2112.02853/) (2021)
- [Proposal-Free Temporal Action Detection via Global Segmentation Mask Learning](https://mlsanity.com/paper/2207.06580/) (2022)
- [RCL: Recurrent Continuous Localization for Temporal Action Detection](https://mlsanity.com/paper/2203.07112/) (2022)
- [ACM-Net: Action Context Modeling Network for Weakly-Supervised Temporal Action Localization](https://mlsanity.com/paper/2104.02967/) (2021)
- [Proposal Relation Network for Temporal Action Detection](https://mlsanity.com/paper/2106.11812/) (2021)
- [ACGNet: Action Complement Graph Network for Weakly-supervised Temporal Action Localization](https://mlsanity.com/paper/2112.10977/) (2021)
- [Weakly-Supervised Temporal Action Localization Through Local-Global Background Modeling](https://mlsanity.com/paper/2106.11811/) (2021)
- [Context-aware Proposal Network for Temporal Action Detection](https://mlsanity.com/paper/2206.09082/) (2022)

## Built on by (most cited first, 16 of 16)

- [Holmes-VAU: Towards Long-term Video Anomaly Understanding at Any Granularity](https://mlsanity.com/paper/2412.06171/) (2024)
- [Holmes-VAD: Towards Unbiased and Explainable Video Anomaly Detection via Multi-modal LLM](https://mlsanity.com/paper/2406.12235/) (2024)
- [Rethinking Pseudo-Label Guided Learning for Weakly Supervised Temporal Action Localization from the Perspective of Noise Correction](https://mlsanity.com/paper/2501.11124/) (2025)
- [GlanceVAD: Exploring Glance Supervision for Label-efficient Video Anomaly Detection](https://mlsanity.com/paper/2403.06154/) (2024)
- [Bridge the Gap: From Weak to Full Supervision for Temporal Action Localization with PseudoFormer](https://mlsanity.com/paper/2504.14860/) (2025)
- [Training-Free Zero-Shot Temporal Action Detection with Vision-Language Models](https://mlsanity.com/paper/2501.13795/) (2025)
- [Full-Stage Pseudo Label Quality Enhancement for Weakly-supervised Temporal Action Localization](https://mlsanity.com/paper/2407.08971/) (2024)
- [Face-Guided Sentiment Boundary Enhancement for Weakly-Supervised Temporal Sentiment Localization](https://mlsanity.com/paper/2603.14750/) (2026)
- [OnPoint: Offline-to-Online Multi-Level Distillation for Point-Supervised Online Temporal Action Localization](https://mlsanity.com/paper/2607.00289/) (2026)
- [Semi-Supervised Pipe Video Temporal Defect Interval Localization](https://mlsanity.com/paper/2407.15170/) (2024)
- [MGCA-Net: Multi-Grained Category-Aware Network for Open-Vocabulary Temporal Action Localization](https://mlsanity.com/paper/2511.13039/) (2025)
- [Point-Supervised Facial Expression Spotting with Gaussian-Based Instance-Adaptive Intensity Modeling](https://mlsanity.com/paper/2511.16952/) (2025)
- [Boosting Point-supervised Temporal Action Localization via Text Refinement and Alignment](https://mlsanity.com/paper/2602.01257/) (2026)
- [Exploring the Temporal Consistency for Point-Level Weakly-Supervised Temporal Action Localization](https://mlsanity.com/paper/2602.05718/) (2026)
- [Decompose and Transfer: CoT-Prompting Enhanced Alignment for Open-Vocabulary Temporal Action Detection](https://mlsanity.com/paper/2603.24030/) (2026)
- [PSEE: Progressive Sensor Event Expansion for Point-Supervised Temporal Action Localization](https://mlsanity.com/paper/2609.21462/) (2026)

## Abstract (arXiv)

Point-supervised Temporal Action Localization (PSTAL) is an emerging research direction for label-efficient learning. However, current methods mainly focus on optimizing the network either at the snippet-level or the instance-level, neglecting the inherent reliability of point annotations at both levels. In this paper, we propose a Hierarchical Reliability Propagation (HR-Pro) framework, which consists of two reliability-aware stages: Snippet-level Discrimination Learning and Instance-level Completeness Learning, both stages explore the efficient propagation of high-confidence cues in point annotations. For snippet-level learning, we introduce an online-updated memory to store reliable snippet prototypes for each class. We then employ a Reliability-aware Attention Block to capture both intra-video and inter-video dependencies of snippets, resulting in more discriminative and robust snippet representation. For instance-level learning, we propose a point-based proposal generation approach as a means of connecting snippets and instances, which produces high-confidence proposals for further optimization at the instance level. Through multi-level reliability-aware learning, we obtain more reliable confidence scores and more accurate temporal boundaries of predicted proposals. Our HR-Pro achieves state-of-the-art performance on multiple challenging benchmarks, including an impressive average mAP of 60.3% on THUMOS14. Notably, our HR-Pro largely surpasses all previous point-supervised methods, and even outperforms several competitive fully supervised methods. Code will be available at https://github.com/pipixin321/HR-Pro.