# CMAE-V: Contrastive Masked Autoencoders for Video Action Recognition

arXiv: https://arxiv.org/abs/2301.06018 · HTML: https://mlsanity.com/paper/2301.06018/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- Builds on 27 of its 29 references that are in the map.
- First posted on arXiv 15 Jan 2023.

## Builds on (most cited first, 27 of 27)

- [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020)
- [Masked Autoencoders Are Scalable Vision Learners](https://mlsanity.com/paper/2111.06377/) (2021)
- [SGDR: Stochastic Gradient Descent with Warm Restarts](https://mlsanity.com/paper/1608.03983/) (2016)
- [Bootstrap your own latent: A new approach to self-supervised Learning](https://mlsanity.com/paper/2006.07733/) (2020)
- [BEiT: BERT Pre-Training of Image Transformers](https://mlsanity.com/paper/2106.08254/) (2021)
- [Non-local Neural Networks](https://mlsanity.com/paper/1711.07971/) (2017)
- [The Kinetics Human Action Video Dataset](https://mlsanity.com/paper/1705.06950/) (2017)
- [SlowFast Networks for Video Recognition](https://mlsanity.com/paper/1812.03982/) (2018)
- [Is Space-Time Attention All You Need for Video Understanding?](https://mlsanity.com/paper/2102.05095/) (2021)
- [ViViT: A Video Vision Transformer](https://mlsanity.com/paper/2103.15691/) (2021)
- [Accurate, Large Minibatch SGD: Training ImageNet in 1 Hour](https://mlsanity.com/paper/1706.02677/) (2017)
- [VideoMAE: Masked Autoencoders are Data-Efficient Learners for Self-Supervised Video Pre-Training](https://mlsanity.com/paper/2203.12602/) (2022)
- [The "something something" video database for learning and evaluating visual common sense](https://mlsanity.com/paper/1706.04261/) (2017)
- [Video Swin Transformer](https://mlsanity.com/paper/2106.13230/) (2021)
- [Multiscale Vision Transformers](https://mlsanity.com/paper/2104.11227/) (2021)
- [TSM: Temporal Shift Module for Efficient Video Understanding](https://mlsanity.com/paper/1811.08383/) (2018)
- [Masked Autoencoders As Spatiotemporal Learners](https://mlsanity.com/paper/2205.09113/) (2022)
- [Video Classification with Channel-Separated Convolutional Networks](https://mlsanity.com/paper/1904.02811/) (2019)
- [Keeping Your Eye on the Ball: Trajectory Attention in Video Transformers](https://mlsanity.com/paper/2106.05392/) (2021)
- [TDN: Temporal Difference Networks for Efficient Action Recognition](https://mlsanity.com/paper/2012.10071/) (2020)
- [BEVT: BERT Pretraining of Video Transformers](https://mlsanity.com/paper/2112.01529/) (2021)
- [TAM: Temporal Adaptive Module for Video Recognition](https://mlsanity.com/paper/2005.06803/) (2020)
- [Contrastive Masked Autoencoders are Stronger Vision Learners](https://mlsanity.com/paper/2207.13532/) (2022)
- [ConvMAE: Masked Convolution Meets Masked Autoencoders](https://mlsanity.com/paper/2205.03892/) (2022)
- [OmniMAE: Single Model Masked Pretraining on Images and Videos](https://mlsanity.com/paper/2206.08356/) (2022)
- [VIMPAC: Video Pre-Training via Masked Token Prediction and Contrastive Learning](https://mlsanity.com/paper/2106.11250/) (2021)
- [Architecture-Agnostic Masked Image Modeling -- From ViT back to CNN](https://mlsanity.com/paper/2205.13943/) (2022)

## Built on by (most cited first, 11 of 11)

- [ViC-MAE: Self-Supervised Representation Learning from Images and Video with Contrastive Masked Autoencoders](https://mlsanity.com/paper/2303.12001/) (2023)
- [ZeroI2V: Zero-Cost Adaptation of Pre-trained Transformers from Image to Video](https://mlsanity.com/paper/2310.01324/) (2023)
- [SIGMA: Sinkhorn-Guided Masked Video Modeling](https://mlsanity.com/paper/2407.15447/) (2024)
- [Masked Modeling for Self-supervised Representation Learning on Vision and Beyond](https://mlsanity.com/paper/2401.00897/) (2023)
- [BirdSAT: Cross-View Contrastive Masked Autoencoders for Bird Species Classification and Mapping](https://mlsanity.com/paper/2310.19168/) (2023)
- [SMILE: Infusing Spatial and Motion Semantics in Masked Video Learning](https://mlsanity.com/paper/2504.00527/) (2025)
- [Animal behavioral analysis and neural encoding with transformer-based self-supervised pretraining](https://mlsanity.com/paper/2507.09513/) (2025)
- [TrackMAE: Video Representation Learning via Track Mask and Predict](https://mlsanity.com/paper/2603.27268/) (2026)
- [Structured-Noise Masked Modeling for Video, Audio and Beyond](https://mlsanity.com/paper/2503.16311/) (2025)
- [A Two-Stage Progressive Pre-training using Multi-Modal Contrastive Masked Autoencoders](https://mlsanity.com/paper/2408.02245/) (2024)
- [Image Classifiers are Efficient Self-Supervised Video Representation Learners](https://mlsanity.com/paper/2609.40347/) (2026)

## Abstract (arXiv)

Contrastive Masked Autoencoder (CMAE), as a new self-supervised framework, has shown its potential of learning expressive feature representations in visual image recognition. This work shows that CMAE also trivially generalizes well on video action recognition without modifying the architecture and the loss criterion. By directly replacing the original pixel shift with the temporal shift, our CMAE for visual action recognition, CMAE-V for short, can generate stronger feature representations than its counterpart based on pure masked autoencoders. Notably, CMAE-V, with a hybrid architecture, can achieve 82.2% and 71.6% top-1 accuracy on the Kinetics-400 and Something-something V2 datasets, respectively. We hope this report could provide some informative inspiration for future works.