# Tiny Video Networks

arXiv: https://arxiv.org/abs/1910.06961 · HTML: https://mlsanity.com/paper/1910.06961/

## Key facts

- Cited by 27 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 15 Oct 2019.

## Built on by (most cited first, 27 of 27)

- [TokenLearner: What Can 8 Learned Tokens Do for Images and Videos?](https://mlsanity.com/paper/2106.11297/) (2021)
- [VidTr: Video Transformer Without Convolutions](https://mlsanity.com/paper/2104.11746/) (2021)
- [A Comprehensive Survey on Hardware-Aware Neural Architecture Search](https://mlsanity.com/paper/2101.09336/) (2021)
- [FrameExit: Conditional Early Exiting for Efficient Video Recognition](https://mlsanity.com/paper/2104.13400/) (2021)
- [AdaMML: Adaptive Multi-Modal Learning for Efficient Video Recognition](https://mlsanity.com/paper/2105.05165/) (2021)
- [Dynamic Network Quantization for Efficient Video Inference](https://mlsanity.com/paper/2108.10394/) (2021)
- [Contrastive Neural Architecture Search with Neural Architecture Comparators](https://mlsanity.com/paper/2103.05471/) (2021)
- [Skip-Convolutions for Efficient Video Processing](https://mlsanity.com/paper/2104.11487/) (2021)
- [Enable Deep Learning on Mobile Devices: Methods, Systems, and Applications](https://mlsanity.com/paper/2204.11786/) (2022)
- [ViPNAS: Efficient Video Pose Estimation via Neural Architecture Search](https://mlsanity.com/paper/2105.10154/) (2021)
- [OCSampler: Compressing Videos to One Clip with Single-step Sampling](https://mlsanity.com/paper/2201.04388/) (2022)
- [SSAN: Separable Self-Attention Network for Video Representation Learning](https://mlsanity.com/paper/2105.13033/) (2021)
- [Beyond Short Clips: End-to-End Video-Level Learning with Collaborative Memories](https://mlsanity.com/paper/2104.01198/) (2021)
- [Hierarchical Compositional Representations for Few-shot Action Recognition](https://mlsanity.com/paper/2208.09424/) (2022)
- [AdaXpert: Adapting Neural Architecture for Growing Data](https://mlsanity.com/paper/2107.00254/) (2021)
- [AZTR: Aerial Video Action Recognition with Auto Zoom and Temporal Reasoning](https://mlsanity.com/paper/2303.01589/) (2023)
- [Visionary: Vision architecture discovery for robot learning](https://mlsanity.com/paper/2103.14633/) (2021)
- [View while Moving: Efficient Video Recognition in Long-untrimmed Videos](https://mlsanity.com/paper/2308.04834/) (2023)
- [Adaptive Intermediate Representations for Video Understanding](https://mlsanity.com/paper/2104.07135/) (2021)
- [Video Action Recognition Using spatio-temporal optical flow video frames](https://mlsanity.com/paper/2103.05101/) (2021)
- [GraphVid: It Only Takes a Few Nodes to Understand a Video](https://mlsanity.com/paper/2207.01375/) (2022)
- [Streamlining Forest Wildfire Surveillance: AI-Enhanced UAVs Utilizing the FLAME Aerial Video Dataset for Lightweight and Efficient Monitoring](https://mlsanity.com/paper/2409.00510/) (2024)
- [Scalable Frame Sampling for Video Classification: A Semi-Optimal Policy Approach with Reduced Search Space](https://mlsanity.com/paper/2409.05260/) (2024)
- [BILLNET: A Binarized Conv3D-LSTM Network with Logic-gated residual architecture for hardware-efficient video inference](https://mlsanity.com/paper/2501.14495/) (2025)
- [Auto-X3D: Ultra-Efficient Video Understanding via Finer-Grained Neural Architecture Search](https://mlsanity.com/paper/2112.04710/) (2021)
- [Two-Stage Framework for Efficient UAV-Based Wildfire Video Analysis with Adaptive Compression and Fire Source Detection](https://mlsanity.com/paper/2508.16739/) (2025)
- [Video Understanding by Design: How Datasets Shape Video Models](https://mlsanity.com/paper/2509.09151/) (2025)

## Abstract (arXiv)

Video understanding is a challenging problem with great impact on the abilities of autonomous agents working in the real-world. Yet, solutions so far have been computationally intensive, with the fastest algorithms running for more than half a second per video snippet on powerful GPUs. We propose a novel idea on video architecture learning - Tiny Video Networks - which automatically designs highly efficient models for video understanding. The tiny video models run with competitive performance for as low as 37 milliseconds per video on a CPU and 10 milliseconds on a standard GPU.