{"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/diffusiontrack-point-set-diffusion-model-for","title":"DiffusionTrack: Point Set Diffusion Model for Visual Object Tracking","arxiv_id":null,"date":"2024-01-01","proceeding":"CVPR 2024 1","authors":["Fei Xie","Zhongdao Wang","Chao Ma"],"abstract":"    Existing Siamese or transformer trackers commonly pose visual object tracking as a one-shot detection problem i.e. locating the target object in a single forward evaluation scheme. Despite the demonstrated success these trackers may easily drift towards distractors with similar appearance due to the single forward evaluation scheme lacking self-correction. To address this issue we cast visual tracking as a point set based denoising diffusion process and propose a novel generative learning based tracker dubbed DiffusionTrack. Our DiffusionTrack possesses two appealing properties: 1) It follows a novel noise-to-target tracking paradigm that leverages multiple denoising diffusion steps to localize the target in a dynamic searching manner per frame. 2) It models the diffusion process using a point set representation which can better handle appearance variations for more precise localization. One side benefit is that DiffusionTrack greatly simplifies the post-processing e.g. removing window penalty scheme. Without bells and whistles our DiffusionTrack achieves leading performance over the state-of-the-art trackers and runs in real-time. The code is in https://github.com/VISION-SJTU/DiffusionTrack.    ","url_abs":"http://openaccess.thecvf.com//content/CVPR2024/html/Xie_DiffusionTrack_Point_Set_Diffusion_Model_for_Visual_Object_Tracking_CVPR_2024_paper.html","url_pdf":"http://openaccess.thecvf.com//content/CVPR2024/papers/Xie_DiffusionTrack_Point_Set_Diffusion_Model_for_Visual_Object_Tracking_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":[],"tasks":[{"task_slug":"denoising","task_name":"Denoising"},{"task_slug":"object-tracking","task_name":"Object Tracking"},{"task_slug":"visual-object-tracking","task_name":"Visual Object Tracking"},{"task_slug":"visual-tracking","task_name":"Visual Tracking"}],"methods":[{"method_slug":"diffusion","method_name":"Diffusion"},{"method_slug":"set","method_name":"SET"}],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/visual-object-tracking-on-vot2022","task":"Visual Object Tracking","dataset":"VOT2022","model":"DiffusionTrack","rank_in_archive_order":4,"of":5,"metrics":{"EAO":"0.634"},"uses_additional_data":false}],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}