mlsanityData to 7 Oct 2026

AutoVideo: An Automated Video Action Recognition System

arXiv 2108.04212 ↗First posted 9 Aug 2021cs.CV cs.LG eess.IVcited by 6

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Abstract (arXiv)

Action recognition is an important task for video understanding with broad applications. However, developing an effective action recognition solution often requires extensive engineering efforts in building and testing different combinations of the modules and their hyperparameters. In this demo, we present AutoVideo, a Python system for automated video action recognition. AutoVideo is featured for 1) highly modular and extendable infrastructure following the standard pipeline language, 2) an exhaustive list of primitives for pipeline construction, 3) data-driven tuners to save the efforts of pipeline tuning, and 4) easy-to-use Graphical User Interface (GUI). AutoVideo is released under MIT license at https://github.com/datamllab/autovideo

Key facts. Cited by 6 papers in the map (data through 7 Oct 2026). Builds on 17 of its 31 references that are in the map. First posted on arXiv 9 Aug 2021.

Builds on

17 of 31 references.

References · 17

  1. 2017Quo Vadis, Action Recognition? A New Model and the Kinetics Datasetcited by 3,259
  2. 2016Temporal Segment Networks: Towards Good Practices for Deep Action Recognitioncited by 899
  3. 2018TSM: Temporal Shift Module for Efficient Video Understandingcited by 852
  4. 2017Ray: A Distributed Framework for Emerging AI Applicationscited by 516
  5. 2017Learning Spatio-Temporal Features with 3D Residual Networks for Action Recognitioncited by 178
  6. 2018Auto-Keras: An Efficient Neural Architecture Search Systemcited by 169
  7. 2018ECO: Efficient Convolutional Network for Online Video Understandingcited by 115
  8. 2021DouZero: Mastering DouDizhu with Self-Play Deep Reinforcement Learningcited by 65
  9. 2016Going Deeper into Action Recognition: A Surveycited by 55
  10. 2019Experience Replay Optimizationcited by 47
  11. 2019AssembleNet: Searching for Multi-Stream Neural Connectivity in Video Architecturescited by 46
  12. 2020TODS: An Automated Time Series Outlier Detection Systemcited by 41
5 more
  1. 2020Meta-AAD: Active Anomaly Detection with Deep Reinforcement Learningcited by 34
  2. 2021Rank the Episodes: A Simple Approach for Exploration in Procedurally-Generated Environmentscited by 28
  3. 2020PyODDS: An End-to-end Outlier Detection System with Automated Machine Learningcited by 21
  4. 2018Evolving Space-Time Neural Architectures for Videoscited by 17
  5. 2019An Efficient 3D CNN for Action/Object Segmentation in Videocited by 10

Built on by

Cited by year

Citing papers per year: 2022: 3; 2023: 3.

6 papers.

  1. 2023Data-centric Artificial Intelligence: A Surveycited by 202
  2. 2023Data-centric AI: Perspectives and Challengescited by 43
  3. 2022AutoShard: Automated Embedding Table Sharding for Recommender Systemscited by 17
  4. 2022DreamShard: Generalizable Embedding Table Placement for Recommender Systemscited by 14
  5. 2022Towards Automated Imbalanced Learning with Deep Hierarchical Reinforcement Learningcited by 9
  6. 2023Towards Personalized Preprocessing Pipeline Searchcited by 1