# Contrastive Positive Mining for Unsupervised 3D Action Representation Learning

arXiv: https://arxiv.org/abs/2208.03497 · HTML: https://mlsanity.com/paper/2208.03497/

## Key facts

- Cited by 34 papers in the map (data through 14 Oct 2026).
- Builds on 31 of its 42 references that are in the map.
- First posted on arXiv 6 Aug 2022.

## Builds on (most cited first, 30 of 31)

- [A Simple Framework for Contrastive Learning of Visual Representations](https://mlsanity.com/paper/2002.05709/) (2020)
- [Representation Learning with Contrastive Predictive Coding](https://mlsanity.com/paper/1807.03748/) (2018)
- [Momentum Contrast for Unsupervised Visual Representation Learning](https://mlsanity.com/paper/1911.05722/) (2019)
- [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)
- [Exploring Simple Siamese Representation Learning](https://mlsanity.com/paper/2011.10566/) (2020)
- [Barlow Twins: Self-Supervised Learning via Redundancy Reduction](https://mlsanity.com/paper/2103.03230/) (2021)
- [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)
- [Spatial Temporal Graph Convolutional Networks for Skeleton-Based Action Recognition](https://mlsanity.com/paper/1801.07455/) (2018)
- [NTU RGB+D: A Large Scale Dataset for 3D Human Activity Analysis](https://mlsanity.com/paper/1604.02808/) (2016)
- [Large Batch Training of Convolutional Networks](https://mlsanity.com/paper/1708.03888/) (2017)
- [NTU RGB+D 120: A Large-Scale Benchmark for 3D Human Activity Understanding](https://mlsanity.com/paper/1905.04757/) (2019)
- [Mean teachers are better role models: Weight-averaged consistency targets improve semi-supervised deep learning results](https://mlsanity.com/paper/1703.01780/) (2017)
- [Unsupervised Learning of Video Representations using LSTMs](https://mlsanity.com/paper/1502.04681/) (2015)
- [An Attention Enhanced Graph Convolutional LSTM Network for Skeleton-Based Action Recognition](https://mlsanity.com/paper/1902.09130/) (2019)
- [SEED: Self-supervised Distillation For Visual Representation](https://mlsanity.com/paper/2101.04731/) (2021)
- [A New Representation of Skeleton Sequences for 3D Action Recognition](https://mlsanity.com/paper/1703.03492/) (2017)
- [3D Human Action Representation Learning via Cross-View Consistency Pursuit](https://mlsanity.com/paper/2104.14466/) (2021)
- [MS2L: Multi-Task Self-Supervised Learning for Skeleton Based Action Recognition](https://mlsanity.com/paper/2010.05599/) (2020)
- [PREDICT & CLUSTER: Unsupervised Skeleton Based Action Recognition](https://mlsanity.com/paper/1911.12409/) (2019)
- [Skeleton-Contrastive 3D Action Representation Learning](https://mlsanity.com/paper/2108.03656/) (2021)
- [Augmented Skeleton Based Contrastive Action Learning with Momentum LSTM for Unsupervised Action Recognition](https://mlsanity.com/paper/2008.00188/) (2020)
- [Iterative Reorganization with Weak Spatial Constraints: Solving Arbitrary Jigsaw Puzzles for Unsupervised Representation Learning](https://mlsanity.com/paper/1812.00329/) (2018)
- [Unsupervised Learning of Long-Term Motion Dynamics for Videos](https://mlsanity.com/paper/1701.01821/) (2017)
- [Unsupervised Feature Learning of Human Actions as Trajectories in Pose Embedding Manifold](https://mlsanity.com/paper/1812.02592/) (2018)
- [Skeleton Image Representation for 3D Action Recognition based on Tree Structure and Reference Joints](https://mlsanity.com/paper/1909.05704/) (2019)
- [Scene Flow to Action Map: A New Representation for RGB-D based Action Recognition with Convolutional Neural Networks](https://mlsanity.com/paper/1702.08652/) (2017)
- [Action Recognition for Depth Video using Multi-view Dynamic Images](https://mlsanity.com/paper/1806.11269/) (2018)
- [Learning and Refining of Privileged Information-based RNNs for Action Recognition from Depth Sequences](https://mlsanity.com/paper/1703.09625/) (2017)

## Built on by (most cited first, 30 of 34)

- [Actionlet-Dependent Contrastive Learning for Unsupervised Skeleton-Based Action Recognition](https://mlsanity.com/paper/2303.10904/) (2023)
- [Masked Motion Predictors are Strong 3D Action Representation Learners](https://mlsanity.com/paper/2308.07092/) (2023)
- [ViA: View-invariant Skeleton Action Representation Learning via Motion Retargeting](https://mlsanity.com/paper/2209.00065/) (2022)
- [Prompted Contrast with Masked Motion Modeling: Towards Versatile 3D Action Representation Learning](https://mlsanity.com/paper/2308.03975/) (2023)
- [Self-Supervised 3D Action Representation Learning with Skeleton Cloud Colorization](https://mlsanity.com/paper/2304.08799/) (2023)
- [USDRL: Unified Skeleton-Based Dense Representation Learning with Multi-Grained Feature Decorrelation](https://mlsanity.com/paper/2412.09220/) (2024)
- [MacDiff: Unified Skeleton Modeling with Masked Conditional Diffusion](https://mlsanity.com/paper/2409.10473/) (2024)
- [Heterogeneous Skeleton-Based Action Representation Learning](https://mlsanity.com/paper/2506.03481/) (2025)
- [Pyramid Self-attention Polymerization Learning for Semi-supervised Skeleton-based Action Recognition](https://mlsanity.com/paper/2302.02327/) (2023)
- [SCD-Net: Spatiotemporal Clues Disentanglement Network for Self-supervised Skeleton-based Action Recognition](https://mlsanity.com/paper/2309.05834/) (2023)
- [Vision-Language Meets the Skeleton: Progressively Distillation with Cross-Modal Knowledge for 3D Action Representation Learning](https://mlsanity.com/paper/2405.20606/) (2024)
- [Foundation Model for Skeleton-Based Human Action Understanding](https://mlsanity.com/paper/2508.12586/) (2025)
- [Identity-free Artificial Emotional Intelligence via Micro-Gesture Understanding](https://mlsanity.com/paper/2405.13206/) (2024)
- [Idempotent Unsupervised Representation Learning for Skeleton-Based Action Recognition](https://mlsanity.com/paper/2410.20349/) (2024)
- [SiMHand: Mining Similar Hands for Large-Scale 3D Hand Pose Pre-training](https://mlsanity.com/paper/2502.15251/) (2025)
- [Skeleton Motion Words for Unsupervised Skeleton-Based Temporal Action Segmentation](https://mlsanity.com/paper/2508.04513/) (2025)
- [Self-Supervised Skeleton-Based Action Representation Learning: A Benchmark and Beyond](https://mlsanity.com/paper/2406.02978/) (2024)
- [Spatio-Temporal Joint Density Driven Learning for Skeleton-Based Action Recognition](https://mlsanity.com/paper/2505.23012/) (2025)
- [STARS: Self-supervised Tuning for 3D Action Recognition in Skeleton Sequences](https://mlsanity.com/paper/2407.10935/) (2024)
- [Towards Efficient General Feature Prediction in Masked Skeleton Modeling](https://mlsanity.com/paper/2509.03609/) (2025)
- [Skeleton2vec: A Self-supervised Learning Framework with Contextualized Target Representations for Skeleton Sequence](https://mlsanity.com/paper/2401.00921/) (2024)
- [Pre-Training for 3D Hand Pose Estimation with Contrastive Learning on Large-Scale Hand Images in the Wild](https://mlsanity.com/paper/2409.09714/) (2024)
- [Attack-Augmentation Mixing-Contrastive Skeletal Representation Learning](https://mlsanity.com/paper/2304.04023/) (2023)
- [Elevating Skeleton-Based Action Recognition with Efficient Multi-Modality Self-Supervision](https://mlsanity.com/paper/2309.12009/) (2023)
- [I2MD: 3D Action Representation Learning with Inter- and Intra-modal Mutual Distillation](https://mlsanity.com/paper/2310.15568/) (2023)
- [Cross-Modal Consistency Learning for Sign Language Recognition](https://mlsanity.com/paper/2503.12485/) (2025)
- [Multimodal Skeleton-Based Action Representation Learning via Decomposition and Composition](https://mlsanity.com/paper/2512.21064/) (2025)
- [Learning Transformation-Isomorphic Latent Space for Accurate Hand Pose Estimation](https://mlsanity.com/paper/2502.12535/) (2025)
- [MS-CLR: Multi-Skeleton Contrastive Learning for Human Action Recognition](https://mlsanity.com/paper/2508.14889/) (2025)
- [Skeleton-to-Image Encoding: Enabling Skeleton Representation Learning via Vision-Pretrained Models](https://mlsanity.com/paper/2603.05963/) (2026)

## Abstract (arXiv)

Recent contrastive based 3D action representation learning has made great progress. However, the strict positive/negative constraint is yet to be relaxed and the use of non-self positive is yet to be explored. In this paper, a Contrastive Positive Mining (CPM) framework is proposed for unsupervised skeleton 3D action representation learning. The CPM identifies non-self positives in a contextual queue to boost learning. Specifically, the siamese encoders are adopted and trained to match the similarity distributions of the augmented instances in reference to all instances in the contextual queue. By identifying the non-self positive instances in the queue, a positive-enhanced learning strategy is proposed to leverage the knowledge of mined positives to boost the robustness of the learned latent space against intra-class and inter-class diversity. Experimental results have shown that the proposed CPM is effective and outperforms the existing state-of-the-art unsupervised methods on the challenging NTU and PKU-MMD datasets.