{"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/poster-v2-a-simpler-and-stronger-facial","title":"POSTER++: A simpler and stronger facial expression recognition network","arxiv_id":"2301.12149","date":"2023-01-28","proceeding":null,"authors":["Jiawei Mao","Rui Xu","Xuesong Yin","Yuanqi Chang","Binling Nie","Aibin Huang"],"abstract":"Facial expression recognition (FER) plays an important role in a variety of real-world applications such as human-computer interaction. POSTER achieves the state-of-the-art (SOTA) performance in FER by effectively combining facial landmark and image features through two-stream pyramid cross-fusion design. However, the architecture of POSTER is undoubtedly complex. It causes expensive computational costs. In order to relieve the computational pressure of POSTER, in this paper, we propose POSTER++. It improves POSTER in three directions: cross-fusion, two-stream, and multi-scale feature extraction. In cross-fusion, we use window-based cross-attention mechanism replacing vanilla cross-attention mechanism. We remove the image-to-landmark branch in the two-stream design. For multi-scale feature extraction, POSTER++ combines images with landmark's multi-scale features to replace POSTER's pyramid design. Extensive experiments on several standard datasets show that our POSTER++ achieves the SOTA FER performance with the minimum computational cost. For example, POSTER++ reached 92.21% on RAF-DB, 67.49% on AffectNet (7 cls) and 63.77% on AffectNet (8 cls), respectively, using only 8.4G floating point operations (FLOPs) and 43.7M parameters (Param). This demonstrates the effectiveness of our improvements.","url_abs":"https://arxiv.org/abs/2301.12149v2","url_pdf":"https://arxiv.org/pdf/2301.12149v2.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":[{"paper_slug":"poster-v2-a-simpler-and-stronger-facial","repo_url":"https://github.com/talented-q/poster_v2","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"facial-expression-recognition-1","task_name":"Facial Expression Recognition"},{"task_slug":"facial-expression-recognition","task_name":"Facial Expression Recognition (FER)"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/facial-expression-recognition-on-affectnet","task":"Facial Expression Recognition (FER)","dataset":"AffectNet","model":"POSTER++","rank_in_archive_order":8,"of":50,"metrics":{"Accuracy (7 emotion)":"67.49","Accuracy (8 emotion)":"63.77"},"uses_additional_data":false},{"leaderboard":"/sota/facial-expression-recognition-on-raf-db","task":"Facial Expression Recognition (FER)","dataset":"RAF-DB","model":"POSTER++","rank_in_archive_order":10,"of":35,"metrics":{"Overall Accuracy":"92.21"},"uses_additional_data":false}],"syntology":{"syntology_url":"https://syntology.ai/paper/2301.12149","atlas_url":"https://app.syntology.ai/?focus=2301.12149","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}