# LOGO-Former: Local-Global Spatio-Temporal Transformer for Dynamic Facial Expression Recognition

arXiv: https://arxiv.org/abs/2305.03343 · HTML: https://mlsanity.com/paper/2305.03343/

## Key facts

- Cited by 15 papers in the map (data through 14 Oct 2026).
- Builds on 17 of its 25 references that are in the map.
- First posted on arXiv 5 May 2023.

## Builds on (most cited first, 17 of 17)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020)
- [Empirical Evaluation of Gated Recurrent Neural Networks on Sequence Modeling](https://mlsanity.com/paper/1412.3555/) (2014)
- [Is Space-Time Attention All You Need for Video Understanding?](https://mlsanity.com/paper/2102.05095/) (2021)
- [Sharpness-Aware Minimization for Efficiently Improving Generalization](https://mlsanity.com/paper/2010.01412/) (2020)
- [A Closer Look at Spatiotemporal Convolutions for Action Recognition](https://mlsanity.com/paper/1711.11248/) (2017)
- [An overview of gradient descent optimization algorithms](https://mlsanity.com/paper/1609.04747/) (2016)
- [MS-Celeb-1M: A Dataset and Benchmark for Large-Scale Face Recognition](https://mlsanity.com/paper/1607.08221/) (2016)
- [Can Spatiotemporal 3D CNNs Retrace the History of 2D CNNs and ImageNet?](https://mlsanity.com/paper/1711.09577/) (2017)
- [Deep Facial Expression Recognition: A Survey](https://mlsanity.com/paper/1804.08348/) (2018)
- [DFEW: A Large-Scale Database for Recognizing Dynamic Facial Expressions in the Wild](https://mlsanity.com/paper/2008.05924/) (2020)
- [Space-time Mixing Attention for Video Transformer](https://mlsanity.com/paper/2106.05968/) (2021)
- [FERV39k: A Large-Scale Multi-Scene Dataset for Facial Expression Recognition in Videos](https://mlsanity.com/paper/2203.09463/) (2022)
- [Facial Expression Recognition with Visual Transformers and Attentional Selective Fusion](https://mlsanity.com/paper/2103.16854/) (2021)
- [Spatio-Temporal Transformer for Dynamic Facial Expression Recognition in the Wild](https://mlsanity.com/paper/2205.04749/) (2022)
- [Temporal Multimodal Fusion for Video Emotion Classification in the Wild](https://mlsanity.com/paper/1709.07200/) (2017)
- [Temporal Stochastic Softmax for 3D CNNs: An Application in Facial Expression Recognition](https://mlsanity.com/paper/2011.05227/) (2020)

## Built on by (most cited first, 15 of 15)

- [Prompting Visual-Language Models for Dynamic Facial Expression Recognition](https://mlsanity.com/paper/2308.13382/) (2023)
- [A3lign-DFER: Pioneering Comprehensive Dynamic Affective Alignment for Dynamic Facial Expression Recognition with CLIP](https://mlsanity.com/paper/2403.04294/) (2024)
- [Static for Dynamic: Towards a Deeper Understanding of Dynamic Facial Expressions Using Static Expression Data](https://mlsanity.com/paper/2409.06154/) (2024)
- [OUS: Scene-Guided Dynamic Facial Expression Recognition](https://mlsanity.com/paper/2405.18769/) (2024)
- [MSSTNet: A Multi-Scale Spatio-Temporal CNN-Transformer Network for Dynamic Facial Expression Recognition](https://mlsanity.com/paper/2404.08433/) (2024)
- [MTCAE-DFER: Multi-Task Cascaded Autoencoder for Dynamic Facial Expression Recognition](https://mlsanity.com/paper/2412.18988/) (2024)
- [Robust Low-Rank Sparse Framework for Video-Based Affective Computing](https://mlsanity.com/paper/2511.11406/) (2025)
- [A Two-Stage Dual-Modality Model for Facial Emotional Expression Recognition](https://mlsanity.com/paper/2603.12221/) (2026)
- [From Coarse to Nuanced: Cross-Modal Alignment of Fine-Grained Linguistic Cues and Visual Salient Regions for Dynamic Emotion Recognition](https://mlsanity.com/paper/2507.11892/) (2025)
- [User Experience Estimation in Human-Robot Interaction Via Multi-Instance Learning of Multimodal Social Signals](https://mlsanity.com/paper/2507.23544/) (2025)
- [MICACL: Multi-Instance Category-Aware Contrastive Learning for Long-Tailed Dynamic Facial Expression Recognition](https://mlsanity.com/paper/2509.04344/) (2025)
- [Text-guided Weakly Supervised Framework for Dynamic Facial Expression Recognition](https://mlsanity.com/paper/2511.10958/) (2025)
- [Bidirectional Learning of Facial Action Units and Expressions via Structured Semantic Mapping across Heterogeneous Datasets](https://mlsanity.com/paper/2604.10541/) (2026)
- [Cognition-Inspired Dual-Stream Semantic Enhancement for Vision-Based Dynamic Emotion Modeling](https://mlsanity.com/paper/2604.12777/) (2026)
- [Reweighting Framewise Attention in Video Transformers for Facial Expression Understanding](https://mlsanity.com/paper/2606.30611/) (2026)

## Abstract (arXiv)

Previous methods for dynamic facial expression recognition (DFER) in the wild are mainly based on Convolutional Neural Networks (CNNs), whose local operations ignore the long-range dependencies in videos. Transformer-based methods for DFER can achieve better performances but result in higher FLOPs and computational costs. To solve these problems, the local-global spatio-temporal Transformer (LOGO-Former) is proposed to capture discriminative features within each frame and model contextual relationships among frames while balancing the complexity. Based on the priors that facial muscles move locally and facial expressions gradually change, we first restrict both the space attention and the time attention to a local window to capture local interactions among feature tokens. Furthermore, we perform the global attention by querying a token with features from each local window iteratively to obtain long-range information of the whole video sequence. In addition, we propose the compact loss regularization term to further encourage the learned features have the minimum intra-class distance and the maximum inter-class distance. Experiments on two in-the-wild dynamic facial expression datasets (i.e., DFEW and FERV39K) indicate that our method provides an effective way to make use of the spatial and temporal dependencies for DFER.