{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/learning-vision-transformer-with-squeeze-and","title":"Learning Vision Transformer with Squeeze and Excitation for Facial Expression Recognition","arxiv_id":"2107.03107","date":"2021-07-07","proceeding":null,"authors":["Mouath Aouayeb","Wassim Hamidouche","Catherine Soladie","Kidiyo Kpalma","Renaud Seguier"],"abstract":"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.","url_abs":"https://arxiv.org/abs/2107.03107v4","url_pdf":"https://arxiv.org/pdf/2107.03107v4.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[],"tasks":[{"task_slug":"facial-expression-recognition","task_name":"Facial Expression Recognition (FER)"}],"methods":[{"method_slug":"absolute-position-encodings","method_name":"Absolute Position Encodings"},{"method_slug":"adam","method_name":"Adam"},{"method_slug":"attention","method_name":"Attention"},{"method_slug":"bpe","method_name":"BPE"},{"method_slug":"convolution","method_name":"Convolution"},{"method_slug":"dense-connections","method_name":"Dense Connections"},{"method_slug":"dropout","method_name":"Dropout"},{"method_slug":"label-smoothing","method_name":"Label Smoothing"},{"method_slug":"layer-normalization","method_name":"Layer Normalization"},{"method_slug":"linear-layer","method_name":"Linear Layer"},{"method_slug":"multi-head-attention","method_name":"Multi-Head Attention"},{"method_slug":"position-wise-feed-forward-layer","method_name":"Position-Wise Feed-Forward Layer"},{"method_slug":"residual-connection","method_name":"Residual Connection"},{"method_slug":"softmax","method_name":"Softmax"},{"method_slug":"transformer","method_name":"Transformer"},{"method_slug":"vision-transformer","method_name":"Vision Transformer"}],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/facial-expression-recognition-on-ck","task":"Facial Expression Recognition (FER)","dataset":"CK+","model":"ViT + SE","rank_in_archive_order":4,"of":7,"metrics":{"Accuracy (7 emotion)":"99.8"},"uses_additional_data":true},{"leaderboard":"/sota/facial-expression-recognition-on-jaffe","task":"Facial Expression Recognition (FER)","dataset":"JAFFE","model":"ViT","rank_in_archive_order":3,"of":4,"metrics":{"Accuracy":"94.83"},"uses_additional_data":false},{"leaderboard":"/sota/facial-expression-recognition-on-rafd","task":"Facial Expression Recognition (FER)","dataset":"RaFD","model":"ViT + SE","rank_in_archive_order":1,"of":1,"metrics":{"Accuracy":"87.22"},"uses_additional_data":false},{"leaderboard":"/sota/facial-expression-recognition-on-sfew","task":"Facial Expression Recognition (FER)","dataset":"SFEW","model":"ViT + SE","rank_in_archive_order":3,"of":4,"metrics":{"Accuracy":"54.29"},"uses_additional_data":false}],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=2107.03107","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}