# An End-to-End Spatio-Temporal Attention Model for Human Action Recognition from Skeleton Data

arXiv: https://arxiv.org/abs/1611.06067 · HTML: https://mlsanity.com/paper/1611.06067/

## Key facts

- Cited by 126 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 18 Nov 2016.

## Built on by (most cited first, 30 of 126)

- [Attention Mechanisms in Computer Vision: A Survey](https://mlsanity.com/paper/2111.07624/) (2021)
- [MotionBERT: A Unified Perspective on Learning Human Motion Representations](https://mlsanity.com/paper/2210.06551/) (2022)
- [BEAT: A Large-Scale Semantic and Emotional Multi-Modal Dataset for Conversational Gestures Synthesis](https://mlsanity.com/paper/2203.05297/) (2022)
- [MSR-GCN: Multi-Scale Residual Graph Convolution Networks for Human Motion Prediction](https://mlsanity.com/paper/2108.07152/) (2021)
- [Constructing Stronger and Faster Baselines for Skeleton-based Action Recognition](https://mlsanity.com/paper/2106.15125/) (2021)
- [Skeleton-Contrastive 3D Action Representation Learning](https://mlsanity.com/paper/2108.03656/) (2021)
- [Multi-Scale Spatial Temporal Graph Convolutional Network for Skeleton-Based Action Recognition](https://mlsanity.com/paper/2206.13028/) (2022)
- [iMiGUE: An Identity-free Video Dataset for Micro-Gesture Understanding and Emotion Analysis](https://mlsanity.com/paper/2107.00285/) (2021)
- [SignBERT: Pre-Training of Hand-Model-Aware Representation for Sign Language Recognition](https://mlsanity.com/paper/2110.05382/) (2021)
- [SignBERT+: Hand-model-aware Self-supervised Pre-training for Sign Language Understanding](https://mlsanity.com/paper/2305.04868/) (2023)
- [Attention, please! A survey of Neural Attention Models in Deep Learning](https://mlsanity.com/paper/2103.16775/) (2021)
- [Generative Action Description Prompts for Skeleton-based Action Recognition](https://mlsanity.com/paper/2208.05318/) (2022)
- [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)
- [Hypergraph Transformer for Skeleton-based Action Recognition](https://mlsanity.com/paper/2211.09590/) (2022)
- [VPN++: Rethinking Video-Pose embeddings for understanding Activities of Daily Living](https://mlsanity.com/paper/2105.08141/) (2021)
- [UNIK: A Unified Framework for Real-world Skeleton-based Action Recognition](https://mlsanity.com/paper/2107.08580/) (2021)
- [Signature Methods in Machine Learning](https://mlsanity.com/paper/2206.14674/) (2022)
- [Towards Consistent Video Editing with Text-to-Image Diffusion Models](https://mlsanity.com/paper/2305.17431/) (2023)
- [Diverse Human Motion Prediction via Gumbel-Softmax Sampling from an Auxiliary Space](https://mlsanity.com/paper/2207.07351/) (2022)
- [IGFormer: Interaction Graph Transformer for Skeleton-based Human Interaction Recognition](https://mlsanity.com/paper/2207.12100/) (2022)
- [Hierarchical Consistent Contrastive Learning for Skeleton-Based Action Recognition with Growing Augmentations](https://mlsanity.com/paper/2211.13466/) (2022)
- [Hierarchically Self-Supervised Transformer for Human Skeleton Representation Learning](https://mlsanity.com/paper/2207.09644/) (2022)
- [Topology-aware Convolutional Neural Network for Efficient Skeleton-based Action Recognition](https://mlsanity.com/paper/2112.04178/) (2021)
- [SkeletonMAE: Spatial-Temporal Masked Autoencoders for Self-supervised Skeleton Action Recognition](https://mlsanity.com/paper/2209.02399/) (2022)
- [BASAR:Black-box Attack on Skeletal Action Recognition](https://mlsanity.com/paper/2103.05266/) (2021)
- [DG-STGCN: Dynamic Spatial-Temporal Modeling for Skeleton-based Action Recognition](https://mlsanity.com/paper/2210.05895/) (2022)
- [Understanding the Robustness of Skeleton-based Action Recognition under Adversarial Attack](https://mlsanity.com/paper/2103.05347/) (2021)
- [Joint-bone Fusion Graph Convolutional Network for Semi-supervised Skeleton Action Recognition](https://mlsanity.com/paper/2202.04075/) (2022)
- [Generative Adversarial Graph Convolutional Networks for Human Action Synthesis](https://mlsanity.com/paper/2110.11191/) (2021)

## Abstract (arXiv)

Human action recognition is an important task in computer vision. Extracting discriminative spatial and temporal features to model the spatial and temporal evolutions of different actions plays a key role in accomplishing this task. In this work, we propose an end-to-end spatial and temporal attention model for human action recognition from skeleton data. We build our model on top of the Recurrent Neural Networks (RNNs) with Long Short-Term Memory (LSTM), which learns to selectively focus on discriminative joints of skeleton within each frame of the inputs and pays different levels of attention to the outputs of different frames. Furthermore, to ensure effective training of the network, we propose a regularized cross-entropy loss to drive the model learning process and develop a joint training strategy accordingly. Experimental results demonstrate the effectiveness of the proposed model,both on the small human action recognition data set of SBU and the currently largest NTU dataset.