{"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/open-world-text-specified-object-counting","title":"Open-world Text-specified Object Counting","arxiv_id":"2306.01851","date":"2023-06-02","proceeding":null,"authors":["Niki Amini-Naieni","Kiana Amini-Naieni","Tengda Han","Andrew Zisserman"],"abstract":"Our objective is open-world object counting in images, where the target object class is specified by a text description. To this end, we propose CounTX, a class-agnostic, single-stage model using a transformer decoder counting head on top of pre-trained joint text-image representations. CounTX is able to count the number of instances of any class given only an image and a text description of the target object class, and can be trained end-to-end. In addition to this model, we make the following contributions: (i) we compare the performance of CounTX to prior work on open-world object counting, and show that our approach exceeds the state of the art on all measures on the FSC-147 benchmark for methods that use text to specify the task; (ii) we present and release FSC-147-D, an enhanced version of FSC-147 with text descriptions, so that object classes can be described with more detailed language than their simple class names. FSC-147-D and the code are available at https://www.robots.ox.ac.uk/~vgg/research/countx.","url_abs":"https://arxiv.org/abs/2306.01851v2","url_pdf":"https://arxiv.org/pdf/2306.01851v2.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":"open-world-text-specified-object-counting","repo_url":"https://github.com/niki-amini-naieni/countx","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"decoder","task_name":"Decoder"},{"task_slug":"object","task_name":"Object"},{"task_slug":"object-counting","task_name":"Object Counting"},{"task_slug":"zero-shot-counting","task_name":"Zero-Shot Counting"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/object-counting-on-carpk","task":"Object Counting","dataset":"CARPK","model":"CounTX (uses arbitrary text input to specify object to count, used \"the cars\" for CARPK)","rank_in_archive_order":8,"of":15,"metrics":{"MAE":"8.13","RMSE":"10.87"},"uses_additional_data":false},{"leaderboard":"/sota/object-counting-on-fsc147","task":"Object Counting","dataset":"FSC147","model":"CounTX (uses text descriptions instead of visual exemplars)","rank_in_archive_order":15,"of":19,"metrics":{"MAE(test)":"15.88","MAE(val)":"17.10","RMSE(test)":"106.29","RMSE(val)":"65.61"},"uses_additional_data":false}],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=2306.01851","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}