# Massively Parallel Video Networks

arXiv: https://arxiv.org/abs/1806.03863 · HTML: https://mlsanity.com/paper/1806.03863/

## Key facts

- Cited by 16 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 11 Jun 2018.

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

- [MeMViT: Memory-Augmented Multiscale Vision Transformer for Efficient Long-Term Video Recognition](https://mlsanity.com/paper/2201.08383/) (2022)
- [MoViNets: Mobile Video Networks for Efficient Video Recognition](https://mlsanity.com/paper/2103.11511/) (2021)
- [Real-time Online Video Detection with Temporal Smoothing Transformers](https://mlsanity.com/paper/2209.09236/) (2022)
- [Learning Local and Global Temporal Contexts for Video Semantic Segmentation](https://mlsanity.com/paper/2204.03330/) (2022)
- [Training a Large Video Model on a Single Machine in a Day](https://mlsanity.com/paper/2309.16669/) (2023)
- [A Coding Framework and Benchmark towards Low-Bitrate Video Understanding](https://mlsanity.com/paper/2202.02813/) (2022)
- [Memory-based Adapters for Online 3D Scene Perception](https://mlsanity.com/paper/2403.06974/) (2024)
- [Learning from One Continuous Video Stream](https://mlsanity.com/paper/2312.00598/) (2023)
- [Continual 3D Convolutional Neural Networks for Real-time Processing of Videos](https://mlsanity.com/paper/2106.00050/) (2021)
- [Efficient High-Resolution Deep Learning: A Survey](https://mlsanity.com/paper/2207.13050/) (2022)
- [Handling Delay in Real-Time Reinforcement Learning](https://mlsanity.com/paper/2503.23478/) (2025)
- [Improving Anytime Prediction with Parallel Cascaded Networks and a Temporal-Difference Loss](https://mlsanity.com/paper/2102.09808/) (2021)
- [Gradient Forward-Propagation for Large-Scale Temporal Video Modelling](https://mlsanity.com/paper/2106.08318/) (2021)
- [A Survey on Video Analytics in Cloud-Edge-Terminal Collaborative Systems](https://mlsanity.com/paper/2502.06581/) (2025)
- [Representation Recycling for Streaming Video Analysis](https://mlsanity.com/paper/2204.13492/) (2022)
- [Scaling Neural Face Synthesis to High FPS and Low Latency by Neural Caching](https://mlsanity.com/paper/2211.05773/) (2022)

## Abstract (arXiv)

We introduce a class of causal video understanding models that aims to improve efficiency of video processing by maximising throughput, minimising latency, and reducing the number of clock cycles. Leveraging operation pipelining and multi-rate clocks, these models perform a minimal amount of computation (e.g. as few as four convolutional layers) for each frame per timestep to produce an output. The models are still very deep, with dozens of such operations being performed but in a pipelined fashion that enables depth-parallel computation. We illustrate the proposed principles by applying them to existing image architectures and analyse their behaviour on two video tasks: action recognition and human keypoint localisation. The results show that a significant degree of parallelism, and implicitly speedup, can be achieved with little loss in performance.