# Learning Vision Transformer with Squeeze and Excitation for Facial Expression Recognition

arXiv: https://arxiv.org/abs/2107.03107 · HTML: https://mlsanity.com/paper/2107.03107/

## Key facts

- Cited by 18 papers in the map (data through 14 Oct 2026).
- Builds on 22 of its 45 references that are in the map.
- First posted on arXiv 7 Jul 2021.

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

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [RoBERTa: A Robustly Optimized BERT Pretraining Approach](https://mlsanity.com/paper/1907.11692/) (2019)
- [End-to-End Object Detection with Transformers](https://mlsanity.com/paper/2005.12872/) (2020)
- [Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization](https://mlsanity.com/paper/1610.02391/) (2016)
- [mixup: Beyond Empirical Risk Minimization](https://mlsanity.com/paper/1710.09412/) (2017)
- [Squeeze-and-Excitation Networks](https://mlsanity.com/paper/1709.01507/) (2017)
- [Training data-efficient image transformers & distillation through attention](https://mlsanity.com/paper/2012.12877/) (2020)
- [Improved Regularization of Convolutional Neural Networks with Cutout](https://mlsanity.com/paper/1708.04552/) (2017)
- [Tokens-to-Token ViT: Training Vision Transformers from Scratch on ImageNet](https://mlsanity.com/paper/2101.11986/) (2021)
- [Revisiting Unreasonable Effectiveness of Data in Deep Learning Era](https://mlsanity.com/paper/1707.02968/) (2017)
- [Score-CAM: Score-Weighted Visual Explanations for Convolutional Neural Networks](https://mlsanity.com/paper/1910.01279/) (2019)
- [AffectNet: A Database for Facial Expression, Valence, and Arousal Computing in the Wild](https://mlsanity.com/paper/1708.03985/) (2017)
- [Eigen-CAM: Class Activation Map using Principal Components](https://mlsanity.com/paper/2008.00299/) (2020)
- [Training Deep Networks for Facial Expression Recognition with Crowd-Sourced Label Distribution](https://mlsanity.com/paper/1608.01041/) (2016)
- [Region Attention Networks for Pose and Occlusion Robust Facial Expression Recognition](https://mlsanity.com/paper/1905.04075/) (2019)
- [Deep-Emotion: Facial Expression Recognition Using Attentional Convolutional Network](https://mlsanity.com/paper/1902.01019/) (2019)
- [Island Loss for Learning Discriminative Features in Facial Expression Recognition](https://mlsanity.com/paper/1710.03144/) (2017)
- [Learning to Amend Facial Expression Representation via De-albino and Affinity](https://mlsanity.com/paper/2103.10189/) (2021)
- [Automatic Facial Expression Recognition Using Features of Salient Facial Patches](https://mlsanity.com/paper/1505.04026/) (2015)
- [Deep Covariance Descriptors for Facial Expression Recognition](https://mlsanity.com/paper/1805.03869/) (2018)

## Built on by (most cited first, 18 of 18)

- [Distract Your Attention: Multi-head Cross Attention Network for Facial Expression Recognition](https://mlsanity.com/paper/2109.07270/) (2021)
- [POSTER: A Pyramid Cross-Fusion Transformer Network for Facial Expression Recognition](https://mlsanity.com/paper/2204.04083/) (2022)
- [Vision Transformer with Attentive Pooling for Robust Facial Expression Recognition](https://mlsanity.com/paper/2212.05463/) (2022)
- [Latent-OFER: Detect, Mask, and Reconstruct with Latent Vectors for Occluded Facial Expression Recognition](https://mlsanity.com/paper/2307.11404/) (2023)
- [Spiking-Fer: Spiking Neural Network for Facial Expression Recognition With Event Cameras](https://mlsanity.com/paper/2304.10211/) (2023)
- [PAtt-Lite: Lightweight Patch and Attention MobileNet for Challenging Facial Expression Recognition](https://mlsanity.com/paper/2306.09626/) (2023)
- [Vision Transformer Equipped with Neural Resizer on Facial Expression Recognition Task](https://mlsanity.com/paper/2204.02181/) (2022)
- [Cluster-level pseudo-labelling for source-free cross-domain facial expression recognition](https://mlsanity.com/paper/2210.05246/) (2022)
- [Facial Expression Analysis and Its Potentials in IoT Systems: A Contemporary Survey](https://mlsanity.com/paper/2412.17616/) (2024)
- [FE-Adapter: Adapting Image-based Emotion Classifiers to Videos](https://mlsanity.com/paper/2408.02421/) (2024)
- [Efficient Neural Architecture Search for Emotion Recognition](https://mlsanity.com/paper/2303.13653/) (2023)
- [GCF: Graph Convolutional Networks for Facial Expression Recognition](https://mlsanity.com/paper/2407.02361/) (2024)
- [Batch Transformer: Look for Attention in Batch](https://mlsanity.com/paper/2407.04218/) (2024)
- [Neural Architecture Search Using Genetic Algorithm for Facial Expression Recognition](https://mlsanity.com/paper/2304.12194/) (2023)
- [Bridging the Gaps: Utilizing Unlabeled Face Recognition Datasets to Boost Semi-Supervised Facial Expression Recognition](https://mlsanity.com/paper/2410.17622/) (2024)
- [e-Inu: Simulating A Quadruped Robot With Emotional Sentience](https://mlsanity.com/paper/2301.00964/) (2023)
- [Facial Expression Recognition using Squeeze and Excitation-powered Swin Transformers](https://mlsanity.com/paper/2301.10906/) (2023)
- [A cross-modal network for facial expression recognition](https://mlsanity.com/paper/2605.04439/) (2026)

## Abstract (arXiv)

As various databases of facial expressions have been made accessible over the last few decades, the Facial Expression Recognition (FER) task has gotten a lot of interest. The multiple sources of the available databases raised several challenges for facial recognition task. These challenges are usually addressed by Convolution Neural Network (CNN) architectures. Different from CNN models, a Transformer model based on attention mechanism has been presented recently to address vision tasks. One of the major issue with Transformers is the need of a large data for training, while most FER databases are limited compared to other vision applications. Therefore, we propose in this paper to learn a vision Transformer jointly with a Squeeze and Excitation (SE) block for FER task. The proposed method is evaluated on different publicly available FER databases including CK+, JAFFE,RAF-DB and SFEW. Experiments demonstrate that our model outperforms state-of-the-art methods on CK+ and SFEW and achieves competitive results on JAFFE and RAF-DB.