# Temporal Context Network for Activity Localization in Videos

arXiv: https://arxiv.org/abs/1708.02349 · HTML: https://mlsanity.com/paper/1708.02349/

## Key facts

- Cited by 37 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 8 Aug 2017.

## Built on by (most cited first, 30 of 37)

- [End-to-end Temporal Action Detection with Transformer](https://mlsanity.com/paper/2106.10271/) (2021)
- [Object-Region Video Transformers](https://mlsanity.com/paper/2110.06915/) (2021)
- [CoLA: Weakly-Supervised Temporal Action Localization with Snippet Contrastive Learning](https://mlsanity.com/paper/2103.16392/) (2021)
- [Generic Event Boundary Detection: A Benchmark for Event Segmentation](https://mlsanity.com/paper/2101.10511/) (2021)
- [Enriching Local and Global Contexts for Temporal Action Localization](https://mlsanity.com/paper/2107.12960/) (2021)
- [Activity Graph Transformer for Temporal Action Localization](https://mlsanity.com/paper/2101.08540/) (2021)
- [Self-Supervised Learning for Semi-Supervised Temporal Action Proposal](https://mlsanity.com/paper/2104.03214/) (2021)
- [Class Semantics-based Attention for Action Detection](https://mlsanity.com/paper/2109.02613/) (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)
- [Graph Convolutional Module for Temporal Action Localization in Videos](https://mlsanity.com/paper/2112.00302/) (2021)
- [Foreground-Action Consistency Network for Weakly Supervised Temporal Action Localization](https://mlsanity.com/paper/2108.06524/) (2021)
- [RCL: Recurrent Continuous Localization for Temporal Action Detection](https://mlsanity.com/paper/2203.07112/) (2022)
- [Self-Feedback DETR for Temporal Action Detection](https://mlsanity.com/paper/2308.10570/) (2023)
- [Few-Shot Temporal Action Localization with Query Adaptive Transformer](https://mlsanity.com/paper/2110.10552/) (2021)
- [Stochastic Backpropagation: A Memory Efficient Strategy for Training Video Models](https://mlsanity.com/paper/2203.16755/) (2022)
- [AOE-Net: Entities Interactions Modeling with Adaptive Attention Mechanism for Temporal Action Proposals Generation](https://mlsanity.com/paper/2210.02578/) (2022)
- [Agent-Environment Network for Temporal Action Proposal Generation](https://mlsanity.com/paper/2107.08323/) (2021)
- [Learning to Discriminate Information for Online Action Detection: Analysis and Application](https://mlsanity.com/paper/2109.03393/) (2021)
- [ABN: Agent-Aware Boundary Networks for Temporal Action Proposal Generation](https://mlsanity.com/paper/2203.08942/) (2022)
- [SegTAD: Precise Temporal Action Detection via Semantic Segmentation](https://mlsanity.com/paper/2203.01542/) (2022)
- [P2ANet: A Dataset and Benchmark for Dense Action Detection from Table Tennis Match Broadcasting Videos](https://mlsanity.com/paper/2207.12730/) (2022)
- [Contextual Explainable Video Representation: Human Perception-based Understanding](https://mlsanity.com/paper/2212.06206/) (2022)
- [M2DAR: Multi-View Multi-Scale Driver Action Recognition with Vision Transformer](https://mlsanity.com/paper/2305.08877/) (2023)
- [Prediction-Feedback DETR for Temporal Action Detection](https://mlsanity.com/paper/2408.16729/) (2024)
- [OWL (Observe, Watch, Listen): Audiovisual Temporal Context for Localizing Actions in Egocentric Videos](https://mlsanity.com/paper/2202.04947/) (2022)
- [Forcing the Whole Video as Background: An Adversarial Learning Strategy for Weakly Temporal Action Localization](https://mlsanity.com/paper/2207.06659/) (2022)
- [Long-term Pre-training for Temporal Action Detection with Transformers](https://mlsanity.com/paper/2408.13152/) (2024)
- [When Spatial meets Temporal in Action Recognition](https://mlsanity.com/paper/2411.15284/) (2024)
- [Generic Event Boundary Detection in Video with Pyramid Features](https://mlsanity.com/paper/2301.04288/) (2023)

## Abstract (arXiv)

We present a Temporal Context Network (TCN) for precise temporal localization of human activities. Similar to the Faster-RCNN architecture, proposals are placed at equal intervals in a video which span multiple temporal scales. We propose a novel representation for ranking these proposals. Since pooling features only inside a segment is not sufficient to predict activity boundaries, we construct a representation which explicitly captures context around a proposal for ranking it. For each temporal segment inside a proposal, features are uniformly sampled at a pair of scales and are input to a temporal convolutional neural network for classification. After ranking proposals, non-maximum suppression is applied and classification is performed to obtain final detections. TCN outperforms state-of-the-art methods on the ActivityNet dataset and the THUMOS14 dataset.