{"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/improving-pedestrian-attribute-recognition","title":"Improving Pedestrian Attribute Recognition With Weakly-Supervised Multi-Scale Attribute-Specific Localization","arxiv_id":"1910.04562","date":"2019-10-10","proceeding":"ICCV 2019 10","authors":["Chufeng Tang","Lu Sheng","Zhao-Xiang Zhang","Xiaolin Hu"],"abstract":"Pedestrian attribute recognition has been an emerging research topic in the area of video surveillance. To predict the existence of a particular attribute, it is demanded to localize the regions related to the attribute. However, in this task, the region annotations are not available. How to carve out these attribute-related regions remains challenging. Existing methods applied attribute-agnostic visual attention or heuristic body-part localization mechanisms to enhance the local feature representations, while neglecting to employ attributes to define local feature areas. We propose a flexible Attribute Localization Module (ALM) to adaptively discover the most discriminative regions and learns the regional features for each attribute at multiple levels. Moreover, a feature pyramid architecture is also introduced to enhance the attribute-specific localization at low-levels with high-level semantic guidance. The proposed framework does not require additional region annotations and can be trained end-to-end with multi-level deep supervision. Extensive experiments show that the proposed method achieves state-of-the-art results on three pedestrian attribute datasets, including PETA, RAP, and PA-100K.","url_abs":"https://arxiv.org/abs/1910.04562v1","url_pdf":"https://arxiv.org/pdf/1910.04562v1.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":"improving-pedestrian-attribute-recognition","repo_url":"https://github.com/chufengt/iccv19_attribute","is_official":1,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok","spdx":"Apache-2.0"}},{"paper_slug":"improving-pedestrian-attribute-recognition","repo_url":"https://github.com/chufengt/alm-pedestrian-attribute","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"attribute","task_name":"Attribute"},{"task_slug":"pedestrian-attribute-recognition","task_name":"Pedestrian Attribute Recognition"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/pedestrian-attribute-recognition-on-pa-100k","task":"Pedestrian Attribute Recognition","dataset":"PA-100K","model":"Attribute-Specific Localization","rank_in_archive_order":10,"of":13,"metrics":{"Accuracy":"77.08%"},"uses_additional_data":false},{"leaderboard":"/sota/pedestrian-attribute-recognition-on-peta","task":"Pedestrian Attribute Recognition","dataset":"PETA","model":"Attribute-Specific Localization","rank_in_archive_order":3,"of":6,"metrics":{"Accuracy":"79.52%"},"uses_additional_data":false},{"leaderboard":"/sota/pedestrian-attribute-recognition-on-rap","task":"Pedestrian Attribute Recognition","dataset":"RAP","model":"Attribute-Specific Localization","rank_in_archive_order":1,"of":2,"metrics":{"Accuracy":"68.17%"},"uses_additional_data":false}],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1910.04562","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1910.04562"}},"developers":"https://syntology.ai/developers","read_at":"2026-09-24T18:15:14+00:00","read_at_is":"when the build read Syntology's graph, not when any sample ran","claim":"Per-sample execution status on synthesized fixtures; not a correctness claim about the paper. 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