# Two-Stream Consensus Network for Weakly-Supervised Temporal Action Localization

arXiv: https://arxiv.org/abs/2010.11594 · HTML: https://mlsanity.com/paper/2010.11594/

## Key facts

- Cited by 53 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 22 Oct 2020.

## Built on by (most cited first, 30 of 53)

- [End-to-end Temporal Action Detection with Transformer](https://mlsanity.com/paper/2106.10271/) (2021)
- [CoLA: Weakly-Supervised Temporal Action Localization with Snippet Contrastive Learning](https://mlsanity.com/paper/2103.16392/) (2021)
- [Enriching Local and Global Contexts for Temporal Action Localization](https://mlsanity.com/paper/2107.12960/) (2021)
- [Learning Action Completeness from Points for Weakly-supervised Temporal Action Localization](https://mlsanity.com/paper/2108.05029/) (2021)
- [TORE: Token Reduction for Efficient Human Mesh Recovery with Transformer](https://mlsanity.com/paper/2211.10705/) (2022)
- [ASM-Loc: Action-aware Segment Modeling for Weakly-Supervised Temporal Action Localization](https://mlsanity.com/paper/2203.15187/) (2022)
- [Cross-modal Consensus Network for Weakly Supervised Temporal Action Localization](https://mlsanity.com/paper/2107.12589/) (2021)
- [Deep Learning-based Action Detection in Untrimmed Videos: A Survey](https://mlsanity.com/paper/2110.00111/) (2021)
- [Weakly Supervised Temporal Action Localization via Representative Snippet Knowledge Propagation](https://mlsanity.com/paper/2203.02925/) (2022)
- [Action Unit Memory Network for Weakly Supervised Temporal Action Localization](https://mlsanity.com/paper/2104.14135/) (2021)
- [Foreground-Action Consistency Network for Weakly Supervised Temporal Action Localization](https://mlsanity.com/paper/2108.06524/) (2021)
- [Augmented Transformer with Adaptive Graph for Temporal Action Proposal Generation](https://mlsanity.com/paper/2103.16024/) (2021)
- [Weakly-Supervised Temporal Action Localization by Inferring Salient Snippet-Feature](https://mlsanity.com/paper/2303.12332/) (2023)
- [Weakly Supervised Action Selection Learning in Video](https://mlsanity.com/paper/2105.02439/) (2021)
- [Background-Click Supervision for Temporal Action Localization](https://mlsanity.com/paper/2111.12449/) (2021)
- [Proposal-Based Multiple Instance Learning for Weakly-Supervised Temporal Action Localization](https://mlsanity.com/paper/2305.17861/) (2023)
- [Adaptive Mutual Supervision for Weakly-Supervised Temporal Action Localization](https://mlsanity.com/paper/2104.02357/) (2021)
- [Learning to Refactor Action and Co-occurrence Features for Temporal Action Localization](https://mlsanity.com/paper/2206.11493/) (2022)
- [High Fidelity 3D Hand Shape Reconstruction via Scalable Graph Frequency Decomposition](https://mlsanity.com/paper/2307.05541/) (2023)
- [ACGNet: Action Complement Graph Network for Weakly-supervised Temporal Action Localization](https://mlsanity.com/paper/2112.10977/) (2021)
- [The Blessings of Unlabeled Background in Untrimmed Videos](https://mlsanity.com/paper/2103.13183/) (2021)
- [Improving Weakly Supervised Temporal Action Localization by Bridging Train-Test Gap in Pseudo Labels](https://mlsanity.com/paper/2304.07978/) (2023)
- [Distilling Vision-Language Pre-training to Collaborate with Weakly-Supervised Temporal Action Localization](https://mlsanity.com/paper/2212.09335/) (2022)
- [SOAR: Scene-debiasing Open-set Action Recognition](https://mlsanity.com/paper/2309.01265/) (2023)
- [Dilated Context Integrated Network with Cross-Modal Consensus for Temporal Emotion Localization in Videos](https://mlsanity.com/paper/2208.01954/) (2022)
- [Weakly Supervised Temporal Action Localization via Dual-Prior Collaborative Learning Guided by Multimodal Large Language Models](https://mlsanity.com/paper/2411.08466/) (2024)
- [Spectrum AUC Difference (SAUCD): Human-aligned 3D Shape Evaluation](https://mlsanity.com/paper/2403.01619/) (2024)
- [Boosting Weakly-Supervised Temporal Action Localization with Text Information](https://mlsanity.com/paper/2305.00607/) (2023)
- [Revisiting Foreground and Background Separation in Weakly-supervised Temporal Action Localization: A Clustering-based Approach](https://mlsanity.com/paper/2312.14138/) (2023)
- [Rethinking Pseudo-Label Guided Learning for Weakly Supervised Temporal Action Localization from the Perspective of Noise Correction](https://mlsanity.com/paper/2501.11124/) (2025)

## Abstract (arXiv)

Weakly-supervised Temporal Action Localization (W-TAL) aims to classify and localize all action instances in an untrimmed video under only video-level supervision. However, without frame-level annotations, it is challenging for W-TAL methods to identify false positive action proposals and generate action proposals with precise temporal boundaries. In this paper, we present a Two-Stream Consensus Network (TSCN) to simultaneously address these challenges. The proposed TSCN features an iterative refinement training method, where a frame-level pseudo ground truth is iteratively updated, and used to provide frame-level supervision for improved model training and false positive action proposal elimination. Furthermore, we propose a new attention normalization loss to encourage the predicted attention to act like a binary selection, and promote the precise localization of action instance boundaries. Experiments conducted on the THUMOS14 and ActivityNet datasets show that the proposed TSCN outperforms current state-of-the-art methods, and even achieves comparable results with some recent fully-supervised methods.