# Multi-Scale Spatio-Temporal Graph Convolutional Network for Facial Expression Spotting

arXiv: https://arxiv.org/abs/2403.15994 · HTML: https://mlsanity.com/paper/2403.15994/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 14 of its 49 references that are in the map.
- First posted on arXiv 24 Mar 2024.

## Builds on (most cited first, 14 of 14)

- [Swin Transformer: Hierarchical Vision Transformer using Shifted Windows](https://mlsanity.com/paper/2103.14030/) (2021)
- [A Simple Framework for Contrastive Learning of Visual Representations](https://mlsanity.com/paper/2002.05709/) (2020)
- [Semi-Supervised Classification with Graph Convolutional Networks](https://mlsanity.com/paper/1609.02907/) (2016)
- [Feature Pyramid Networks for Object Detection](https://mlsanity.com/paper/1612.03144/) (2016)
- [Supervised Contrastive Learning](https://mlsanity.com/paper/2004.11362/) (2020)
- [Graph Stacked Hourglass Networks for 3D Human Pose Estimation](https://mlsanity.com/paper/2103.16385/) (2021)
- [Survey on RGB, 3D, Thermal, and Multimodal Approaches for Facial Expression Recognition: History, Trends, and Affect-related Applications](https://mlsanity.com/paper/1606.03237/) (2016)
- [Relation Modeling with Graph Convolutional Networks for Facial Action Unit Detection](https://mlsanity.com/paper/1910.10334/) (2019)
- [SAMM Long Videos: A Spontaneous Facial Micro- and Macro-Expressions Dataset](https://mlsanity.com/paper/1911.01519/) (2019)
- [Micro-expression detection in long videos using optical flow and recurrent neural networks](https://mlsanity.com/paper/1903.10765/) (2019)
- [3D-CNN for Facial Micro- and Macro-expression Spotting on Long Video Sequences using Temporal Oriented Reference Frame](https://mlsanity.com/paper/2105.06340/) (2021)
- [Shallow Optical Flow Three-Stream CNN for Macro- and Micro-Expression Spotting from Long Videos](https://mlsanity.com/paper/2106.06489/) (2021)
- [Spotting Macro- and Micro-expression Intervals in Long Video Sequences](https://mlsanity.com/paper/1912.11985/) (2019)
- [AU-aware graph convolutional network for Macro- and Micro-expression spotting](https://mlsanity.com/paper/2303.09114/) (2023)

## Built on by (most cited first, 4 of 4)

- [Exploring Timeline Control for Facial Motion Generation](https://mlsanity.com/paper/2505.20861/) (2025)
- [SpotFormer: Multi-Scale Spatio-Temporal Transformer for Facial Expression Spotting](https://mlsanity.com/paper/2407.20799/) (2024)
- [GCF: Graph Convolutional Networks for Facial Expression Recognition](https://mlsanity.com/paper/2407.02361/) (2024)
- [Point-Supervised Facial Expression Spotting with Gaussian-Based Instance-Adaptive Intensity Modeling](https://mlsanity.com/paper/2511.16952/) (2025)

## Abstract (arXiv)

Facial expression spotting is a significant but challenging task in facial expression analysis. The accuracy of expression spotting is affected not only by irrelevant facial movements but also by the difficulty of perceiving subtle motions in micro-expressions. In this paper, we propose a Multi-Scale Spatio-Temporal Graph Convolutional Network (SpoT-GCN) for facial expression spotting. To extract more robust motion features, we track both short- and long-term motion of facial muscles in compact sliding windows whose window length adapts to the temporal receptive field of the network. This strategy, termed the receptive field adaptive sliding window strategy, effectively magnifies the motion features while alleviating the problem of severe head movement. The subtle motion features are then converted to a facial graph representation, whose spatio-temporal graph patterns are learned by a graph convolutional network. This network learns both local and global features from multiple scales of facial graph structures using our proposed facial local graph pooling (FLGP). Furthermore, we introduce supervised contrastive learning to enhance the discriminative capability of our model for difficult-to-classify frames. The experimental results on the SAMM-LV and CAS(ME)^2 datasets demonstrate that our method achieves state-of-the-art performance, particularly in micro-expression spotting. Ablation studies further verify the effectiveness of our proposed modules.