MCG-NJU/VideoMAE
[NeurIPS 2022 Spotlight] VideoMAE: Masked Autoencoders are Data-Efficient Learners for Self-Supervised Video Pre-Training observed · 2026-08-28
Health v2 · maintenance only
32/100
- Activity 0
- Release rhythm 35
- Longevity 100
Flags: no_releases no_license
How is this computed?
round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10) — computed 2026-09-02. Adoption (stars, forks) is never an input.
- gap_med: n/a
- age_days: 1624
- days_rel: n/a
- days_push: 999
- n_releases_24m: 0
Adoption not part of the score
1784 stars · 171 forks observed · 2026-08-28
What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-30, confidence not recorded
Official PyTorch implementation of VideoMAE, a masked autoencoder method for data-efficient self-supervised video pre-training with video tube masking. It provides pre-trained ViT-based video models and training/finetuning code for action recognition benchmarks like Kinetics-400, Something-Something V2, UCF101, and HMDB51.
Use cases
- pre-train video transformers self-supervised on small video datasets
- fine-tune video models for action recognition
- classify human actions in videos
- extract video representations for downstream tasks
- reproduce NeurIPS 2022 VideoMAE results
- train video models without extra labeled data
When to choose
- you need data-efficient self-supervised video pre-training on limited data
- you want strong action recognition backbones with released checkpoints
- you are researching masked autoencoders for video
When to avoid
- you need a permissively licensed model for commercial use (CC BY-NC 4.0)
- you need real-time video inference in production
- you work outside PyTorch
Facets
library · maturity maintenance
machine-learning deep-learning video-processing computer-vision machine-learning computer-vision artificial-intelligence python masked-autoencoder self-supervised-learning video-transformer action-recognition pre-training pytorch vision-transformer video gpu
6 sources
- readme: https://github.com/MCG-NJU/VideoMAE · fetched 2026-08-28 · 303f1494ab48
- homepage: https://arxiv.org/abs/2203.12602 · fetched 2026-08-29 · 287998c80b5e
- site_page: https://info.arxiv.org/about/donate.html · fetched 2026-08-29 · cca9c3a11c56
- site_page: https://info.arxiv.org/about/ourmembers.html · fetched 2026-08-29 · 47cbc55ff1de
- site_page: https://info.arxiv.org/about · fetched 2026-08-29 · a1f16f915a9a
- site_page: https://info.arxiv.org/labs/index.html · fetched 2026-08-29 · b14a8d05a0ec
Member repositories
| Repository | Role | Health v2 |
|---|---|---|
| MCG-NJU/VideoMAE | main | 32 |
For agents
markdown · JSON · MCP: product_card(name="MCG-NJU/VideoMAE")
Data as of 2026-08-30T08:39:29.467469+00:00 · Report a problem