# Weakly-supervised Action Localization with Background Modeling

arXiv: https://arxiv.org/abs/1908.06552 · HTML: https://mlsanity.com/paper/1908.06552/

## Key facts

- Cited by 53 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 19 Aug 2019.

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

- [CoLA: Weakly-Supervised Temporal Action Localization with Snippet Contrastive Learning](https://mlsanity.com/paper/2103.16392/) (2021)
- [A Hybrid Attention Mechanism for Weakly-Supervised Temporal Action Localization](https://mlsanity.com/paper/2101.00545/) (2021)
- [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)
- [Drop-DTW: Aligning Common Signal Between Sequences While Dropping Outliers](https://mlsanity.com/paper/2108.11996/) (2021)
- [Weakly Supervised Temporal Adjacent Network for Language Grounding](https://mlsanity.com/paper/2106.16136/) (2021)
- [Fine-grained Temporal Contrastive Learning for Weakly-supervised Temporal Action Localization](https://mlsanity.com/paper/2203.16800/) (2022)
- [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)
- [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)
- [ACSNet: Action-Context Separation Network for Weakly Supervised Temporal Action Localization](https://mlsanity.com/paper/2103.15088/) (2021)
- [Proposal-Based Multiple Instance Learning for Weakly-Supervised Temporal Action Localization](https://mlsanity.com/paper/2305.17861/) (2023)
- [ACM-Net: Action Context Modeling Network for Weakly-Supervised Temporal Action Localization](https://mlsanity.com/paper/2104.02967/) (2021)
- [Multi-modal Prompting for Low-Shot Temporal Action Localization](https://mlsanity.com/paper/2303.11732/) (2023)
- [Adaptive Mutual Supervision for Weakly-Supervised Temporal Action Localization](https://mlsanity.com/paper/2104.02357/) (2021)
- [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)
- [Temporal Action Proposal Generation with Background Constraint](https://mlsanity.com/paper/2112.07984/) (2021)
- [NSNet: Non-saliency Suppression Sampler for Efficient Video Recognition](https://mlsanity.com/paper/2207.10388/) (2022)
- [Improving Weakly Supervised Temporal Action Localization by Bridging Train-Test Gap in Pseudo Labels](https://mlsanity.com/paper/2304.07978/) (2023)
- [Weakly Supervised Temporal Action Localization Through Learning Explicit Subspaces for Action and Context](https://mlsanity.com/paper/2103.16155/) (2021)
- [Distilling Vision-Language Pre-training to Collaborate with Weakly-Supervised Temporal Action Localization](https://mlsanity.com/paper/2212.09335/) (2022)
- [Hierarchical Modeling for Task Recognition and Action Segmentation in Weakly-Labeled Instructional Videos](https://mlsanity.com/paper/2110.05697/) (2021)
- [Anchor-Constrained Viterbi for Set-Supervised Action Segmentation](https://mlsanity.com/paper/2104.02113/) (2021)
- [Revisiting Foreground and Background Separation in Weakly-supervised Temporal Action Localization: A Clustering-based Approach](https://mlsanity.com/paper/2312.14138/) (2023)
- [Deep Motion Prior for Weakly-Supervised Temporal Action Localization](https://mlsanity.com/paper/2108.05607/) (2021)
- [Weakly-Supervised Temporal Action Localization by Progressive Complementary Learning](https://mlsanity.com/paper/2206.11011/) (2022)

## Abstract (arXiv)

We describe a latent approach that learns to detect actions in long sequences given training videos with only whole-video class labels. Our approach makes use of two innovations to attention-modeling in weakly-supervised learning. First, and most notably, our framework uses an attention model to extract both foreground and background frames whose appearance is explicitly modeled. Most prior works ignore the background, but we show that modeling it allows our system to learn a richer notion of actions and their temporal extents. Second, we combine bottom-up, class-agnostic attention modules with top-down, class-specific activation maps, using the latter as form of self-supervision for the former. Doing so allows our model to learn a more accurate model of attention without explicit temporal supervision. These modifications lead to 10% AP@IoU=0.5 improvement over existing systems on THUMOS14. Our proposed weaklysupervised system outperforms recent state-of-the-arts by at least 4.3% AP@IoU=0.5. Finally, we demonstrate that weakly-supervised learning can be used to aggressively scale-up learning to in-the-wild, uncurated Instagram videos. The addition of these videos significantly improves localization performance of our weakly-supervised model