# Motion-R3: Fast and Accurate Motion Annotation via Representation-based Representativeness Ranking

arXiv: https://arxiv.org/abs/2304.01672 · HTML: https://mlsanity.com/paper/2304.01672/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 27 of its 67 references that are in the map.
- First posted on arXiv 4 Apr 2023.

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

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- [Contrastive Multiview Coding](https://mlsanity.com/paper/1906.05849/) (2019)
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- [Colorful Image Colorization](https://mlsanity.com/paper/1603.08511/) (2016)
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- [BABEL: Bodies, Action and Behavior with English Labels](https://mlsanity.com/paper/2106.09696/) (2021)
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- [Contrastive Learning from Extremely Augmented Skeleton Sequences for Self-supervised Action Recognition](https://mlsanity.com/paper/2112.03590/) (2021)
- [Spatial Temporal Transformer Network for Skeleton-based Action Recognition](https://mlsanity.com/paper/2012.06399/) (2020)
- [Skeleton-Contrastive 3D Action Representation Learning](https://mlsanity.com/paper/2108.03656/) (2021)
- [Co-occurrence Feature Learning for Skeleton based Action Recognition using Regularized Deep LSTM Networks](https://mlsanity.com/paper/1603.07772/) (2016)
- [Skeleton-Based Human Action Recognition with Global Context-Aware Attention LSTM Networks](https://mlsanity.com/paper/1707.05740/) (2017)
- [Augmented Skeleton Based Contrastive Action Learning with Momentum LSTM for Unsupervised Action Recognition](https://mlsanity.com/paper/2008.00188/) (2020)
- [Online Human Action Detection using Joint Classification-Regression Recurrent Neural Networks](https://mlsanity.com/paper/1604.05633/) (2016)
- [Human Activity Recognition using Recurrent Neural Networks](https://mlsanity.com/paper/1804.07144/) (2018)
- [Bootstrapped Representation Learning for Skeleton-Based Action Recognition](https://mlsanity.com/paper/2202.02232/) (2022)
- [Learning Multi-level Features For Sensor-based Human Action Recognition](https://mlsanity.com/paper/1611.07143/) (2016)

## Abstract (arXiv)

In this paper, we follow a data-centric philosophy and propose a novel motion annotation method based on the inherent representativeness of motion data in a given dataset. Specifically, we propose a Representation-based Representativeness Ranking R3 method that ranks all motion data in a given dataset according to their representativeness in a learned motion representation space. We further propose a novel dual-level motion constrastive learning method to learn the motion representation space in a more informative way. Thanks to its high efficiency, our method is particularly responsive to frequent requirements change and enables agile development of motion annotation models. Experimental results on the HDM05 dataset against state-of-the-art methods demonstrate the superiority of our method.