{"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/sparse-local-patch-transformer-for-robust","title":"Sparse Local Patch Transformer for Robust Face Alignment and Landmarks Inherent Relation Learning","arxiv_id":"2203.06541","date":"2022-03-13","proceeding":"CVPR 2022 1","authors":["Jiahao Xia","Weiwei qu","Wenjian Huang","JianGuo Zhang","Xi Wang","Min Xu"],"abstract":"Heatmap regression methods have dominated face alignment area in recent years while they ignore the inherent relation between different landmarks. In this paper, we propose a Sparse Local Patch Transformer (SLPT) for learning the inherent relation. The SLPT generates the representation of each single landmark from a local patch and aggregates them by an adaptive inherent relation based on the attention mechanism. The subpixel coordinate of each landmark is predicted independently based on the aggregated feature. Moreover, a coarse-to-fine framework is further introduced to incorporate with the SLPT, which enables the initial landmarks to gradually converge to the target facial landmarks using fine-grained features from dynamically resized local patches. Extensive experiments carried out on three popular benchmarks, including WFLW, 300W and COFW, demonstrate that the proposed method works at the state-of-the-art level with much less computational complexity by learning the inherent relation between facial landmarks. The code is available at the project website.","url_abs":"https://arxiv.org/abs/2203.06541v2","url_pdf":"https://arxiv.org/pdf/2203.06541v2.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":"sparse-local-patch-transformer-for-robust","repo_url":"https://github.com/jiahao-uts/slpt-master","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"face-alignment","task_name":"Face Alignment"},{"task_slug":null,"task_name":"Relation"},{"task_slug":"robust-face-alignment","task_name":"Robust Face Alignment"}],"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":"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"}],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/face-alignment-on-300w","task":"Face Alignment","dataset":"300W","model":"SLPT","rank_in_archive_order":17,"of":48,"metrics":{"NME_inter-ocular (%, Challenge)":"4.90","NME_inter-ocular (%, Common)":"2.75","NME_inter-ocular (%, Full)":"3.17"},"uses_additional_data":false},{"leaderboard":"/sota/face-alignment-on-cofw","task":"Face Alignment","dataset":"COFW","model":"SLPT","rank_in_archive_order":9,"of":28,"metrics":{"NME (inter-ocular)":"3.32","NME (inter-pupil)":"4.79"},"uses_additional_data":false},{"leaderboard":"/sota/face-alignment-on-cofw-68","task":"Face Alignment","dataset":"COFW-68","model":"SPLT","rank_in_archive_order":2,"of":7,"metrics":{"NME (inter-ocular)":"4.10"},"uses_additional_data":false},{"leaderboard":"/sota/face-alignment-on-wflw","task":"Face Alignment","dataset":"WFLW","model":"SLPT","rank_in_archive_order":10,"of":36,"metrics":{"AUC@10 (inter-ocular)":"59.5","FR@10 (inter-ocular)":"2.76","NME (inter-ocular)":"4.14"},"uses_additional_data":false},{"leaderboard":"/sota/face-alignment-on-wfw-extra-data","task":"Face Alignment","dataset":"WFW (Extra Data)","model":"SPLT","rank_in_archive_order":6,"of":11,"metrics":{"AUC@10 (inter-ocular)":"59.50","FR@10 (inter-ocular)":"2.76","NME (inter-ocular)":"4.14"},"uses_additional_data":false}],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=2203.06541","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}