# Self-Supervised Video Representation Learning with Motion-Contrastive Perception

arXiv: https://arxiv.org/abs/2204.04607 · HTML: https://mlsanity.com/paper/2204.04607/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 29 of its 35 references that are in the map.
- First posted on arXiv 10 Apr 2022.

## Builds on (most cited first, 29 of 29)

- [A Simple Framework for Contrastive Learning of Visual Representations](https://mlsanity.com/paper/2002.05709/) (2020)
- [Momentum Contrast for Unsupervised Visual Representation Learning](https://mlsanity.com/paper/1911.05722/) (2019)
- [Quo Vadis, Action Recognition? A New Model and the Kinetics Dataset](https://mlsanity.com/paper/1705.07750/) (2017)
- [FaceNet: A Unified Embedding for Face Recognition and Clustering](https://mlsanity.com/paper/1503.03832/) (2015)
- [Unsupervised Representation Learning by Predicting Image Rotations](https://mlsanity.com/paper/1803.07728/) (2018)
- [Unsupervised Learning of Visual Representations by Solving Jigsaw Puzzles](https://mlsanity.com/paper/1603.09246/) (2016)
- [Contrastive Multiview Coding](https://mlsanity.com/paper/1906.05849/) (2019)
- [A Closer Look at Spatiotemporal Convolutions for Action Recognition](https://mlsanity.com/paper/1711.11248/) (2017)
- [Unsupervised Visual Representation Learning by Context Prediction](https://mlsanity.com/paper/1505.05192/) (2015)
- [Can Spatiotemporal 3D CNNs Retrace the History of 2D CNNs and ImageNet?](https://mlsanity.com/paper/1711.09577/) (2017)
- [Rethinking Spatiotemporal Feature Learning: Speed-Accuracy Trade-offs in Video Classification](https://mlsanity.com/paper/1712.04851/) (2017)
- [Learning Representations for Automatic Colorization](https://mlsanity.com/paper/1603.06668/) (2016)
- [Self-supervised Co-training for Video Representation Learning](https://mlsanity.com/paper/2010.09709/) (2020)
- [Unsupervised Representation Learning by Sorting Sequences](https://mlsanity.com/paper/1708.01246/) (2017)
- [Multi-task Self-Supervised Visual Learning](https://mlsanity.com/paper/1708.07860/) (2017)
- [Video Representation Learning by Dense Predictive Coding](https://mlsanity.com/paper/1909.04656/) (2019)
- [SpeedNet: Learning the Speediness in Videos](https://mlsanity.com/paper/2004.06130/) (2020)
- [Self-Supervised Video Representation Learning with Space-Time Cubic Puzzles](https://mlsanity.com/paper/1811.09795/) (2018)
- [Self-supervised Video Representation Learning by Pace Prediction](https://mlsanity.com/paper/2008.05861/) (2020)
- [Video Playback Rate Perception for Self-supervisedSpatio-Temporal Representation Learning](https://mlsanity.com/paper/2006.11476/) (2020)
- [RSPNet: Relative Speed Perception for Unsupervised Video Representation Learning](https://mlsanity.com/paper/2011.07949/) (2020)
- [Video Cloze Procedure for Self-Supervised Spatio-Temporal Learning](https://mlsanity.com/paper/2001.00294/) (2020)
- [Self-Supervised Spatiotemporal Feature Learning via Video Rotation Prediction](https://mlsanity.com/paper/1811.11387/) (2018)
- [Removing the Background by Adding the Background: Towards Background Robust Self-supervised Video Representation Learning](https://mlsanity.com/paper/2009.05769/) (2020)
- [Self-supervised Video Representation Learning Using Inter-intra Contrastive Framework](https://mlsanity.com/paper/2008.02531/) (2020)
- [Improving Spatiotemporal Self-Supervision by Deep Reinforcement Learning](https://mlsanity.com/paper/1807.11293/) (2018)
- [ASCNet: Self-supervised Video Representation Learning with Appearance-Speed Consistency](https://mlsanity.com/paper/2106.02342/) (2021)
- [Self-supervised Video Representation Learning by Context and Motion Decoupling](https://mlsanity.com/paper/2104.00862/) (2021)
- [Rethinking Motion Representation: Residual Frames with 3D ConvNets for Better Action Recognition](https://mlsanity.com/paper/2001.05661/) (2020)

## Built on by (most cited first, 1 of 1)

- [Modality-Aware Contrastive Instance Learning with Self-Distillation for Weakly-Supervised Audio-Visual Violence Detection](https://mlsanity.com/paper/2207.05500/) (2022)

## Abstract (arXiv)

Visual-only self-supervised learning has achieved significant improvement in video representation learning. Existing related methods encourage models to learn video representations by utilizing contrastive learning or designing specific pretext tasks. However, some models are likely to focus on the background, which is unimportant for learning video representations. To alleviate this problem, we propose a new view called long-range residual frame to obtain more motion-specific information. Based on this, we propose the Motion-Contrastive Perception Network (MCPNet), which consists of two branches, namely, Motion Information Perception (MIP) and Contrastive Instance Perception (CIP), to learn generic video representations by focusing on the changing areas in videos. Specifically, the MIP branch aims to learn fine-grained motion features, and the CIP branch performs contrastive learning to learn overall semantics information for each instance. Experiments on two benchmark datasets UCF-101 and HMDB-51 show that our method outperforms current state-of-the-art visual-only self-supervised approaches.