{"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/you-only-learn-one-query-learning-unified","title":"You Only Learn One Query: Learning Unified Human Query for Single-Stage Multi-Person Multi-Task Human-Centric Perception","arxiv_id":"2312.05525","date":"2023-12-09","proceeding":null,"authors":["Sheng Jin","Shuhuai Li","Tong Li","Wentao Liu","Chen Qian","Ping Luo"],"abstract":"Human-centric perception (e.g. detection, segmentation, pose estimation, and attribute analysis) is a long-standing problem for computer vision. This paper introduces a unified and versatile framework (HQNet) for single-stage multi-person multi-task human-centric perception (HCP). Our approach centers on learning a unified human query representation, denoted as Human Query, which captures intricate instance-level features for individual persons and disentangles complex multi-person scenarios. Although different HCP tasks have been well-studied individually, single-stage multi-task learning of HCP tasks has not been fully exploited in the literature due to the absence of a comprehensive benchmark dataset. To address this gap, we propose COCO-UniHuman benchmark to enable model development and comprehensive evaluation. Experimental results demonstrate the proposed method's state-of-the-art performance among multi-task HCP models and its competitive performance compared to task-specific HCP models. Moreover, our experiments underscore Human Query's adaptability to new HCP tasks, thus demonstrating its robust generalization capability. Codes and data are available at https://github.com/lishuhuai527/COCO-UniHuman.","url_abs":"https://arxiv.org/abs/2312.05525v3","url_pdf":"https://arxiv.org/pdf/2312.05525v3.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":"you-only-learn-one-query-learning-unified","repo_url":"https://github.com/lishuhuai527/coco-unihuman","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok"}}],"tasks":[{"task_slug":"attribute","task_name":"Attribute"},{"task_slug":"human-instance-segmentation","task_name":"Human Instance Segmentation"},{"task_slug":"multi-task-learning","task_name":"Multi-Task Learning"},{"task_slug":"pose-estimation","task_name":"Pose Estimation"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/human-instance-segmentation-on-ochuman","task":"Human Instance Segmentation","dataset":"OCHuman","model":"HQNet (ResNet-50)","rank_in_archive_order":3,"of":18,"metrics":{"AP":"31.1"},"uses_additional_data":false},{"leaderboard":"/sota/pose-estimation-on-ochuman","task":"Pose Estimation","dataset":"OCHuman","model":"HQNet (ViT-L)","rank_in_archive_order":7,"of":19,"metrics":{"Test AP":"45.6"},"uses_additional_data":false},{"leaderboard":"/sota/pose-estimation-on-ochuman","task":"Pose Estimation","dataset":"OCHuman","model":"HQNet (ResNet-50)","rank_in_archive_order":11,"of":19,"metrics":{"Test AP":"40.0"},"uses_additional_data":false}],"syntology":{"syntology_url":null,"atlas_url":"https://app.syntology.ai/?focus=2312.05525","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}