# Temporal Gaussian Mixture Layer for Videos

arXiv: https://arxiv.org/abs/1803.06316 · HTML: https://mlsanity.com/paper/1803.06316/

## Key facts

- Cited by 40 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 16 Mar 2018.

## Built on by (most cited first, 30 of 40)

- [Language Repository for Long Video Understanding](https://mlsanity.com/paper/2403.14622/) (2024)
- [Activity Graph Transformer for Temporal Action Localization](https://mlsanity.com/paper/2101.08540/) (2021)
- [MS-TCT: Multi-Scale Temporal ConvTransformer for Action Detection](https://mlsanity.com/paper/2112.03902/) (2021)
- [An Empirical Study of End-to-End Temporal Action Detection](https://mlsanity.com/paper/2204.02932/) (2022)
- [Modeling Multi-Label Action Dependencies for Temporal Action Localization](https://mlsanity.com/paper/2103.03027/) (2021)
- [Learning an Augmented RGB Representation with Cross-Modal Knowledge Distillation for Action Detection](https://mlsanity.com/paper/2108.03619/) (2021)
- [Graph Convolutional Module for Temporal Action Localization in Videos](https://mlsanity.com/paper/2112.00302/) (2021)
- [Coarse-Fine Networks for Temporal Activity Detection in Videos](https://mlsanity.com/paper/2103.01302/) (2021)
- [VicTR: Video-conditioned Text Representations for Activity Recognition](https://mlsanity.com/paper/2304.02560/) (2023)
- [Action Sensitivity Learning for Temporal Action Localization](https://mlsanity.com/paper/2305.15701/) (2023)
- [Token Turing Machines](https://mlsanity.com/paper/2211.09119/) (2022)
- [PointTAD: Multi-Label Temporal Action Detection with Learnable Query Points](https://mlsanity.com/paper/2210.11035/) (2022)
- [Dual DETRs for Multi-Label Temporal Action Detection](https://mlsanity.com/paper/2404.00653/) (2024)
- [SVIP: Sequence VerIfication for Procedures in Videos](https://mlsanity.com/paper/2112.06447/) (2021)
- [UniMD: Towards Unifying Moment Retrieval and Temporal Action Detection](https://mlsanity.com/paper/2404.04933/) (2024)
- [CTRN: Class-Temporal Relational Network for Action Detection](https://mlsanity.com/paper/2110.13473/) (2021)
- [LAC: Latent Action Composition for Skeleton-based Action Segmentation](https://mlsanity.com/paper/2308.14500/) (2023)
- [UnweaveNet: Unweaving Activity Stories](https://mlsanity.com/paper/2112.10194/) (2021)
- [Benchmarking the Robustness of Temporal Action Detection Models Against Temporal Corruptions](https://mlsanity.com/paper/2403.20254/) (2024)
- [Gaussian Mixture Proposals with Pull-Push Learning Scheme to Capture Diverse Events for Weakly Supervised Temporal Video Grounding](https://mlsanity.com/paper/2312.16388/) (2023)
- [PAT: Position-Aware Transformer for Dense Multi-Label Action Detection](https://mlsanity.com/paper/2308.05051/) (2023)
- [Weakly-guided Self-supervised Pretraining for Temporal Activity Detection](https://mlsanity.com/paper/2111.13675/) (2021)
- [Weakly-Supervised Temporal Action Localization by Progressive Complementary Learning](https://mlsanity.com/paper/2206.11011/) (2022)
- [Temporal Action Detection Model Compression by Progressive Block Drop](https://mlsanity.com/paper/2503.16916/) (2025)
- [Image Modeling with Deep Convolutional Gaussian Mixture Models](https://mlsanity.com/paper/2104.12686/) (2021)
- [CNN-Based Action Recognition and Pose Estimation for Classifying Animal Behavior from Videos: A Survey](https://mlsanity.com/paper/2301.06187/) (2023)
- [Animal Behavior Analysis Methods Using Deep Learning: A Survey](https://mlsanity.com/paper/2405.14002/) (2024)
- [Transform-Equivariant Consistency Learning for Temporal Sentence Grounding](https://mlsanity.com/paper/2305.04123/) (2023)
- [PAMI-AD: An Activity Detector Exploiting Part-attention and Motion Information in Surveillance Videos](https://mlsanity.com/paper/2203.03796/) (2022)
- [MS-Temba: Multi-Scale Temporal Mamba for Understanding Long Untrimmed Videos](https://mlsanity.com/paper/2501.06138/) (2025)

## Abstract (arXiv)

We introduce a new convolutional layer named the Temporal Gaussian Mixture (TGM) layer and present how it can be used to efficiently capture longer-term temporal information in continuous activity videos. The TGM layer is a temporal convolutional layer governed by a much smaller set of parameters (e.g., location/variance of Gaussians) that are fully differentiable. We present our fully convolutional video models with multiple TGM layers for activity detection. The extensive experiments on multiple datasets, including Charades and MultiTHUMOS, confirm the effectiveness of TGM layers, significantly outperforming the state-of-the-arts.