{"type":"ScholarlyArticle","title":"Revisiting Continual Ultra-fine-grained Visual Recognition with Pre-trained Models","authors":[{"name":"Pengcheng Zhang","slug":"pengcheng-zhang","given":"Pengcheng","family":"Zhang","affiliation":""},{"name":"Xiaohan Yu","slug":"xiaohan-yu","given":"Xiaohan","family":"Yu","affiliation":""},{"name":"Meiying Gu","slug":"meiying-gu","given":"Meiying","family":"Gu","affiliation":""},{"name":"Yuchen Wu","slug":"yuchen-wu","given":"Yuchen","family":"Wu","affiliation":""},{"name":"Yongsheng Gao","slug":"yongsheng-gao","given":"Yongsheng","family":"Gao","affiliation":""},{"name":"Xiao Bai","slug":"xiao-bai","given":"Xiao","family":"Bai","affiliation":""}],"abstract":"Continual ultra-fine-grained visual recognition (C-UFG) aims to continuously learn to categorize the increasing number of cultivates (VC-UFG) and consistently recognize crops across reproductive stages (HC-UFG), which is a fundamental goal of intelligent agriculture. Despite the progress made in general continual learning, C-UFG remains an underexplored issue. This work establishes the first comprehensive C-UFG benchmark using massive soy leaf data. By analyzing recent pre-trained model (PTM) based continual learning methods on the proposed benchmark, we propose two simple yet effective PTM-based methods to boost the performance of VC-UFG and HC-UFG, respectively. On top of those, we integrate the two methods into one unified framework and propose the first unified model, Unic, that is capable of tackling the C-UFG problem where VC-UFG and HC-UFG co-exist in a single continual learning sequence. To understand the effectiveness of the proposed methods, we first evaluate the models on VC-UFG and HC-UFG challenges and then test the proposed Unic on a unified C-UFG challenge. Experimental results demonstrate the proposed methods achieve superior performance for C-UFG. The code is available at https://github.com/PatrickZad/unicufg.","doi":"10.24963/ijcai.2025/1053","url":"https://ijcai.org/proceedings/2025/1053","pdf":"https://ijcai.org/proceedings/2025/1053.pdf","markdown":"https://ijcai.org/proceedings/2025/1053.md","bibtex":"https://ijcai.org/proceedings/2025/bibtex/1053","volume":{"slug":"2025","title":"Proceedings of the Thirty-Fourth International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2025","url":"https://ijcai.org/proceedings/2025/","year":2025,"location":"Montreal, Canada","dates":"16-22 August 2025","editors":"James Kwok","isbn":"978-1-956792-06-5","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2025"},"section":"AI4Tech: AI Enabling Technologies","track":"AI4Tech: AI Enabling Technologies","number":1053,"pages":{"first":9474,"last":9482},"keywords":["Domain-specific AI4Tech: AI4Agriculture"],"videos":[],"references":[],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}