{"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/from-open-set-to-closed-set-counting-objects","title":"From Open Set to Closed Set: Counting Objects by Spatial Divide-and-Conquer","arxiv_id":"1908.06473","date":"2019-08-15","proceeding":"ICCV 2019 10","authors":["Haipeng Xiong","Hao Lu","Chengxin Liu","Liang Liu","Zhiguo Cao","Chunhua Shen"],"abstract":"Visual counting, a task that predicts the number of objects from an image/video, is an open-set problem by nature, i.e., the number of population can vary in $[0,+\\infty)$ in theory. However, the collected images and labeled count values are limited in reality, which means only a small closed set is observed. Existing methods typically model this task in a regression manner, while they are likely to suffer from an unseen scene with counts out of the scope of the closed set. In fact, counting is decomposable. A dense region can always be divided until sub-region counts are within the previously observed closed set. Inspired by this idea, we propose a simple but effective approach, Spatial Divide-and- Conquer Network (S-DCNet). S-DCNet only learns from a closed set but can generalize well to open-set scenarios via S-DC. S-DCNet is also efficient. To avoid repeatedly computing sub-region convolutional features, S-DC is executed on the feature map instead of on the input image. S-DCNet achieves the state-of-the-art performance on three crowd counting datasets (ShanghaiTech, UCF_CC_50 and UCF-QNRF), a vehicle counting dataset (TRANCOS) and a plant counting dataset (MTC). Compared to the previous best methods, S-DCNet brings a 20.2% relative improvement on the ShanghaiTech Part B, 20.9% on the UCF-QNRF, 22.5% on the TRANCOS and 15.1% on the MTC. Code has been made available at: https://github. com/xhp-hust-2018-2011/S-DCNet.","url_abs":"https://arxiv.org/abs/1908.06473v1","url_pdf":"https://arxiv.org/pdf/1908.06473v1.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":"from-open-set-to-closed-set-counting-objects","repo_url":"https://github.com/xhp-hust-2018-2011/S-DCNet","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok","spdx":"MIT"}},{"paper_slug":"from-open-set-to-closed-set-counting-objects","repo_url":"https://github.com/dmburd/S-DCNet","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok"}},{"paper_slug":"from-open-set-to-closed-set-counting-objects","repo_url":"https://github.com/jani-excergy/Crowd_Density_Estimation","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":null},{"paper_slug":"from-open-set-to-closed-set-counting-objects","repo_url":"https://github.com/karanjsingh/S-DCNet_CrowdCount","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok","spdx":"MIT"}},{"paper_slug":"from-open-set-to-closed-set-counting-objects","repo_url":"https://github.com/MohamedAliRashad/Crowd-DCNet","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"pytorch","reach":{"status":"ok"}}],"tasks":[{"task_slug":"crowd-counting","task_name":"Crowd Counting"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/crowd-counting-on-shanghaitech-a","task":"Crowd Counting","dataset":"ShanghaiTech A","model":"S-DCNet","rank_in_archive_order":14,"of":35,"metrics":{"MAE":"58.3"},"uses_additional_data":false},{"leaderboard":"/sota/crowd-counting-on-shanghaitech-b","task":"Crowd Counting","dataset":"ShanghaiTech B","model":"S-DCNet","rank_in_archive_order":11,"of":32,"metrics":{"MAE":"6.7"},"uses_additional_data":true},{"leaderboard":"/sota/crowd-counting-on-trancos","task":"Crowd Counting","dataset":"TRANCOS","model":"S-DCNet","rank_in_archive_order":3,"of":4,"metrics":{"MAE":"2.92"},"uses_additional_data":false}],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1908.06473","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1908.06473"}},"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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