{"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/weakly-supervised-video-individual-counting","title":"Weakly Supervised Video Individual Counting","arxiv_id":null,"date":"2024-01-01","proceeding":"CVPR 2024 1","authors":["Xinyan Liu","Guorong Li","Yuankai Qi","Ziheng Yan","Zhenjun Han","Anton Van Den Hengel","Ming-Hsuan Yang","Qingming Huang"],"abstract":"    Video Individual Counting (VIC) aims to predict the number of unique individuals in a single video. Existing methods learn representations based on trajectory labels for individuals which are annotation-expensive. To provide a more realistic reflection of the underlying practical challenge we introduce a weakly supervised VIC task wherein trajectory labels are not provided. Instead two types of labels are provided to indicate traffic entering the field of view (inflow) and leaving the field view (outflow). We also propose the first solution as a baseline that formulates the task as a weakly supervised contrastive learning problem under group-level matching. In doing so we devise an end-to-end trainable soft contrastive loss to drive the network to distinguish inflow outflow and the remaining. To facilitate future study in this direction we generate annotations from the existing VIC datasets SenseCrowd and CroHD and also build a new dataset UAVVIC. Extensive results show that our baseline weakly supervised method outperforms supervised methods and thus little information is lost in the transition to the more practically relevant weakly supervised task. The code and trained model can be found at CGNet.    ","url_abs":"http://openaccess.thecvf.com//content/CVPR2024/html/Liu_Weakly_Supervised_Video_Individual_Counting_CVPR_2024_paper.html","url_pdf":"http://openaccess.thecvf.com//content/CVPR2024/papers/Liu_Weakly_Supervised_Video_Individual_Counting_CVPR_2024_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":"weakly-supervised-video-individual-counting","repo_url":"https://github.com/streamer-ap/cgnet","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"contrastive-learning","task_name":"Contrastive Learning"},{"task_slug":"video-individual-counting","task_name":"Video Individual Counting"}],"methods":[{"method_slug":"contrastive-learning","method_name":"Contrastive Learning"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}