# MSA-GCN:Multiscale Adaptive Graph Convolution Network for Gait Emotion Recognition

arXiv: https://arxiv.org/abs/2209.08988 · HTML: https://mlsanity.com/paper/2209.08988/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 22 of its 65 references that are in the map.
- First posted on arXiv 19 Sep 2022.

## Builds on (most cited first, 22 of 22)

- [Going Deeper with Convolutions](https://mlsanity.com/paper/1409.4842/) (2014)
- [CBAM: Convolutional Block Attention Module](https://mlsanity.com/paper/1807.06521/) (2018)
- [Spatial Temporal Graph Convolutional Networks for Skeleton-Based Action Recognition](https://mlsanity.com/paper/1801.07455/) (2018)
- [Temporal Segment Networks: Towards Good Practices for Deep Action Recognition](https://mlsanity.com/paper/1608.00859/) (2016)
- [Disentangling and Unifying Graph Convolutions for Skeleton-Based Action Recognition](https://mlsanity.com/paper/2003.14111/) (2020)
- [Channel-wise Topology Refinement Graph Convolution for Skeleton-Based Action Recognition](https://mlsanity.com/paper/2107.12213/) (2021)
- [3D Human Pose Estimation with Spatial and Temporal Transformers](https://mlsanity.com/paper/2103.10455/) (2021)
- [Spatio-Temporal LSTM with Trust Gates for 3D Human Action Recognition](https://mlsanity.com/paper/1607.07043/) (2016)
- [GaitSet: Regarding Gait as a Set for Cross-View Gait Recognition](https://mlsanity.com/paper/1811.06186/) (2018)
- [Dynamic Multiscale Graph Neural Networks for 3D Skeleton-Based Human Motion Prediction](https://mlsanity.com/paper/2003.08802/) (2020)
- [GaitGraph: Graph Convolutional Network for Skeleton-Based Gait Recognition](https://mlsanity.com/paper/2101.11228/) (2021)
- [Emotion-Cause Pair Extraction: A New Task to Emotion Analysis in Texts](https://mlsanity.com/paper/1906.01267/) (2019)
- [Towards a Deeper Understanding of Skeleton-based Gait Recognition](https://mlsanity.com/paper/2204.07855/) (2022)
- [Going Deeper in Facial Expression Recognition using Deep Neural Networks](https://mlsanity.com/paper/1511.04110/) (2015)
- [STEP: Spatial Temporal Graph Convolutional Networks for Emotion Perception from Gaits](https://mlsanity.com/paper/1910.12906/) (2019)
- [ProxEmo: Gait-based Emotion Learning and Multi-view Proxemic Fusion for Socially-Aware Robot Navigation](https://mlsanity.com/paper/2003.01062/) (2020)
- [Identifying Emotions from Walking using Affective and Deep Features](https://mlsanity.com/paper/1906.11884/) (2019)
- [FedNoiL: A Simple Two-Level Sampling Method for Federated Learning with Noisy Labels](https://mlsanity.com/paper/2205.10110/) (2022)
- [SemiNLL: A Framework of Noisy-Label Learning by Semi-Supervised Learning](https://mlsanity.com/paper/2012.00925/) (2020)
- [Take an Emotion Walk: Perceiving Emotions from Gaits Using Hierarchical Attention Pooling and Affective Mapping](https://mlsanity.com/paper/1911.08708/) (2019)
- [Action Recognition via Pose-Based Graph Convolutional Networks with Intermediate Dense Supervision](https://mlsanity.com/paper/1911.12509/) (2019)
- [EVA: Generating Emotional Behavior of Virtual Agents using Expressive Features of Gait and Gaze](https://mlsanity.com/paper/1907.02102/) (2019)

## Built on by (most cited first, 6 of 6)

- [Emotion Recognition from Skeleton Data: A Comprehensive Survey](https://mlsanity.com/paper/2507.18026/) (2025)
- [Self-supervised Gait-based Emotion Representation Learning from Selective Strongly Augmented Skeleton Sequences](https://mlsanity.com/paper/2405.04900/) (2024)
- [Detail-Enhanced Intra- and Inter-modal Interaction for Audio-Visual Emotion Recognition](https://mlsanity.com/paper/2405.16701/) (2024)
- [Incorporating Scene Context and Semantic Labels for Enhanced Group-level Emotion Recognition](https://mlsanity.com/paper/2509.21747/) (2025)
- [CGTGait: Collaborative Graph and Transformer for Gait Emotion Recognition](https://mlsanity.com/paper/2509.16623/) (2025)
- [Single-Stream Multi-Feature Fusion with Temporal Robustness for Gait Emotion Recognition](https://mlsanity.com/paper/2609.11680/) (2026)

## Abstract (arXiv)

Gait emotion recognition plays a crucial role in the intelligent system. Most of the existing methods recognize emotions by focusing on local actions over time. However, they ignore that the effective distances of different emotions in the time domain are different, and the local actions during walking are quite similar. Thus, emotions should be represented by global states instead of indirect local actions. To address these issues, a novel Multi Scale Adaptive Graph Convolution Network (MSA-GCN) is presented in this work through constructing dynamic temporal receptive fields and designing multiscale information aggregation to recognize emotions. In our model, a adaptive selective spatial-temporal graph convolution is designed to select the convolution kernel dynamically to obtain the soft spatio-temporal features of different emotions. Moreover, a Cross-Scale mapping Fusion Mechanism (CSFM) is designed to construct an adaptive adjacency matrix to enhance information interaction and reduce redundancy. Compared with previous state-of-the-art methods, the proposed method achieves the best performance on two public datasets, improving the mAP by 2%. We also conduct extensive ablations studies to show the effectiveness of different components in our methods.