{"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/unconstrained-fashion-landmark-detection-via","title":"Unconstrained Fashion Landmark Detection via Hierarchical Recurrent Transformer Networks","arxiv_id":"1708.02044","date":"2017-08-07","proceeding":null,"authors":["Sijie Yan","Ziwei Liu","Ping Luo","Shi Qiu","Xiaogang Wang","Xiaoou Tang"],"abstract":"Fashion landmarks are functional key points defined on clothes, such as\ncorners of neckline, hemline, and cuff. They have been recently introduced as\nan effective visual representation for fashion image understanding. However,\ndetecting fashion landmarks are challenging due to background clutters, human\nposes, and scales. To remove the above variations, previous works usually\nassumed bounding boxes of clothes are provided in training and test as\nadditional annotations, which are expensive to obtain and inapplicable in\npractice. This work addresses unconstrained fashion landmark detection, where\nclothing bounding boxes are not provided in both training and test. To this\nend, we present a novel Deep LAndmark Network (DLAN), where bounding boxes and\nlandmarks are jointly estimated and trained iteratively in an end-to-end\nmanner. DLAN contains two dedicated modules, including a Selective Dilated\nConvolution for handling scale discrepancies, and a Hierarchical Recurrent\nSpatial Transformer for handling background clutters. To evaluate DLAN, we\npresent a large-scale fashion landmark dataset, namely Unconstrained Landmark\nDatabase (ULD), consisting of 30K images. Statistics show that ULD is more\nchallenging than existing datasets in terms of image scales, background\nclutters, and human poses. Extensive experiments demonstrate the effectiveness\nof DLAN over the state-of-the-art methods. DLAN also exhibits excellent\ngeneralization across different clothing categories and modalities, making it\nextremely suitable for real-world fashion analysis.","url_abs":"http://arxiv.org/abs/1708.02044v1","url_pdf":"http://arxiv.org/pdf/1708.02044v1.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":"unconstrained-fashion-landmark-detection-via","repo_url":"https://github.com/fdjingyuan/Deep-Fashion-Analysis-ECCV2018","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":null},{"paper_slug":"unconstrained-fashion-landmark-detection-via","repo_url":"https://github.com/shumming/GLE_FLD","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":null}],"tasks":[],"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":"relu","method_name":"ReLU"},{"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":[],"syntology":{"syntology_url":null,"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}