{"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/what-can-help-pedestrian-detection","title":"What Can Help Pedestrian Detection?","arxiv_id":"1705.02757","date":"2017-05-08","proceeding":"CVPR 2017 7","authors":["Jiayuan Mao","Tete Xiao","Yuning Jiang","Zhimin Cao"],"abstract":"Aggregating extra features has been considered as an effective approach to\nboost traditional pedestrian detection methods. However, there is still a lack\nof studies on whether and how CNN-based pedestrian detectors can benefit from\nthese extra features. The first contribution of this paper is exploring this\nissue by aggregating extra features into CNN-based pedestrian detection\nframework. Through extensive experiments, we evaluate the effects of different\nkinds of extra features quantitatively. Moreover, we propose a novel network\narchitecture, namely HyperLearner, to jointly learn pedestrian detection as\nwell as the given extra feature. By multi-task training, HyperLearner is able\nto utilize the information of given features and improve detection performance\nwithout extra inputs in inference. The experimental results on multiple\npedestrian benchmarks validate the effectiveness of the proposed HyperLearner.","url_abs":"http://arxiv.org/abs/1705.02757v1","url_pdf":"http://arxiv.org/pdf/1705.02757v1.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":[],"tasks":[{"task_slug":"pedestrian-detection","task_name":"Pedestrian Detection"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/pedestrian-detection-on-caltech","task":"Pedestrian Detection","dataset":"Caltech","model":"HyperLearner","rank_in_archive_order":15,"of":33,"metrics":{"Reasonable Miss Rate":"5.5"},"uses_additional_data":false}],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1705.02757","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}