{"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/referring-expression-counting","title":"Referring Expression Counting","arxiv_id":null,"date":"2024-01-01","proceeding":"CVPR 2024 1","authors":["Siyang Dai","Jun Liu","Ngai-Man Cheung"],"abstract":"    Existing counting tasks are limited to the class level which don't account for fine-grained details within the class. In real applications it often requires in-context or referring human input for counting target objects. Take urban analysis as an example fine-grained information such as traffic flow in different directions pedestrians and vehicles waiting or moving at different sides of the junction is more beneficial. Current settings of both class-specific and class-agnostic counting treat objects of the same class indifferently which pose limitations in real use cases. To this end we propose a new task named Referring Expression Counting (REC) which aims to count objects with different attributes within the same class. To evaluate the REC task we create a novel dataset named REC-8K which contains 8011 images and 17122 referring expressions. Experiments on REC-8K show that our proposed method achieves state-of-the-art performance compared with several text-based counting methods and an open-set object detection model. We also outperform prior models on the class agnostic counting (CAC) benchmark [36] for the zero-shot setting and perform on par with the few-shot methods. Code and dataset is available at https://github.com/sydai/referring-expression-counting.    ","url_abs":"http://openaccess.thecvf.com//content/CVPR2024/html/Dai_Referring_Expression_Counting_CVPR_2024_paper.html","url_pdf":"http://openaccess.thecvf.com//content/CVPR2024/papers/Dai_Referring_Expression_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":"referring-expression-counting","repo_url":"https://github.com/sydai/referring-expression-counting","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":null,"task_name":"8k"},{"task_slug":"object-detection","task_name":"Object Detection"},{"task_slug":"referring-expression","task_name":"Referring Expression"},{"task_slug":"object-detection-1","task_name":"object-detection"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}