{"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/free-lunch-enhancements-for-multi-modal-crowd","title":"Free Lunch Enhancements for Multi-modal Crowd Counting","arxiv_id":null,"date":"2025-01-01","proceeding":"CVPR 2025 1","authors":["Haoliang Meng","Xiaopeng Hong","Zhengqin Lai","Miao Shang"],"abstract":"    This paper addresses multi-modal crowd counting with a novel `free lunch' training enhancement strategy that requires no additional data, parameters, or increased inference complexity. First, we introduce a cross-modal alignment technique as a plug-in post-processing step for the pre-trained backbone network, enhancing the model's ability to capture shared information across modalities. Second, we incorporate a regional density supervision mechanism during the fine-tuning stage, which differentiates features in regions with varying crowd densities. Extensive experiments on three multi-modal crowd counting datasets validate our approach, making it the first to achieve an MAE below 10 on RGBT-CC. The code is available at https://github.com/HenryCilence/Free-Lunch-Multimodal-Counting.    ","url_abs":"http://openaccess.thecvf.com//content/CVPR2025/html/Meng_Free_Lunch_Enhancements_for_Multi-modal_Crowd_Counting_CVPR_2025_paper.html","url_pdf":"http://openaccess.thecvf.com//content/CVPR2025/papers/Meng_Free_Lunch_Enhancements_for_Multi-modal_Crowd_Counting_CVPR_2025_paper.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":"free-lunch-enhancements-for-multi-modal-crowd","repo_url":"https://github.com/henrycilence/free-lunch-multimodal-counting","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"pytorch","reach":{"status":"ok"}}],"tasks":[{"task_slug":"crowd-counting","task_name":"Crowd Counting"},{"task_slug":"cross-modal-alignment","task_name":"cross-modal alignment"}],"methods":[{"method_slug":"mae","method_name":"MAE"}],"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}