# Spatial Hierarchy and Temporal Attention Guided Cross Masking for Self-supervised Skeleton-based Action Recognition

arXiv: https://arxiv.org/abs/2409.17951 · HTML: https://mlsanity.com/paper/2409.17951/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 30 of its 41 references that are in the map.
- First posted on arXiv 26 Sep 2024.

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

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Masked Autoencoders Are Scalable Vision Learners](https://mlsanity.com/paper/2111.06377/) (2021)
- [NTU RGB+D: A Large Scale Dataset for 3D Human Activity Analysis](https://mlsanity.com/paper/1604.02808/) (2016)
- [NTU RGB+D 120: A Large-Scale Benchmark for 3D Human Activity Understanding](https://mlsanity.com/paper/1905.04757/) (2019)
- [Hyperbolic Neural Networks](https://mlsanity.com/paper/1805.09112/) (2018)
- [Hyperbolic Graph Neural Networks](https://mlsanity.com/paper/1910.12892/) (2019)
- [MotionBERT: A Unified Perspective on Learning Human Motion Representations](https://mlsanity.com/paper/2210.06551/) (2022)
- [3D Human Action Representation Learning via Cross-View Consistency Pursuit](https://mlsanity.com/paper/2104.14466/) (2021)
- [PKU-MMD: A Large Scale Benchmark for Continuous Multi-Modal Human Action Understanding](https://mlsanity.com/paper/1703.07475/) (2017)
- [PREDICT & CLUSTER: Unsupervised Skeleton Based Action Recognition](https://mlsanity.com/paper/1911.12409/) (2019)
- [Contrastive Learning from Extremely Augmented Skeleton Sequences for Self-supervised Action Recognition](https://mlsanity.com/paper/2112.03590/) (2021)
- [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)
- [Spatiotemporal Co-attention Recurrent Neural Networks for Human-Skeleton Motion Prediction](https://mlsanity.com/paper/1909.13245/) (2019)
- [HYperbolic Self-Paced Learning for Self-Supervised Skeleton-based Action Representations](https://mlsanity.com/paper/2303.06242/) (2023)
- [Actionlet-Dependent Contrastive Learning for Unsupervised Skeleton-Based Action Recognition](https://mlsanity.com/paper/2303.10904/) (2023)
- [SkeletonMAE: Graph-based Masked Autoencoder for Skeleton Sequence Pre-training](https://mlsanity.com/paper/2307.08476/) (2023)
- [Masked Motion Predictors are Strong 3D Action Representation Learners](https://mlsanity.com/paper/2308.07092/) (2023)
- [CMD: Self-supervised 3D Action Representation Learning with Cross-modal Mutual Distillation](https://mlsanity.com/paper/2208.12448/) (2022)
- [Hierarchical Consistent Contrastive Learning for Skeleton-Based Action Recognition with Growing Augmentations](https://mlsanity.com/paper/2211.13466/) (2022)
- [Hierarchical Contrast for Unsupervised Skeleton-based Action Representation Learning](https://mlsanity.com/paper/2212.02082/) (2022)
- [SkeletonMAE: Spatial-Temporal Masked Autoencoders for Self-supervised Skeleton Action Recognition](https://mlsanity.com/paper/2209.02399/) (2022)
- [HaLP: Hallucinating Latent Positives for Skeleton-based Self-Supervised Learning of Actions](https://mlsanity.com/paper/2304.00387/) (2023)
- [Part Aware Contrastive Learning for Self-Supervised Action Recognition](https://mlsanity.com/paper/2305.00666/) (2023)
- [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)
- [Skeleton-aided Articulated Motion Generation](https://mlsanity.com/paper/1707.01058/) (2017)
- [SCD-Net: Spatiotemporal Clues Disentanglement Network for Self-supervised Skeleton-based Action Recognition](https://mlsanity.com/paper/2309.05834/) (2023)
- [Dynamic Hyperbolic Attention Network for Fine Hand-object Reconstruction](https://mlsanity.com/paper/2309.02965/) (2023)
- [Skeleton2vec: A Self-supervised Learning Framework with Contextualized Target Representations for Skeleton Sequence](https://mlsanity.com/paper/2401.00921/) (2024)

## Abstract (arXiv)

In self-supervised skeleton-based action recognition, the mask reconstruction paradigm is gaining interest in enhancing model refinement and robustness through effective masking. However, previous works primarily relied on a single masking criterion, resulting in the model overfitting specific features and overlooking other effective information. In this paper, we introduce a hierarchy and attention guided cross-masking framework (HA-CM) that applies masking to skeleton sequences from both spatial and temporal perspectives. Specifically, in spatial graphs, we utilize hyperbolic space to maintain joint distinctions and effectively preserve the hierarchical structure of high-dimensional skeletons, employing joint hierarchy as the masking criterion. In temporal flows, we substitute traditional distance metrics with the global attention of joints for masking, addressing the convergence of distances in high-dimensional space and the lack of a global perspective. Additionally, we incorporate cross-contrast loss based on the cross-masking framework into the loss function to enhance the model's learning of instance-level features. HA-CM shows efficiency and universality on three public large-scale datasets, NTU-60, NTU-120, and PKU-MMD. The source code of our HA-CM is available at https://github.com/YinxPeng/HA-CM-main.