---
title: "Revisiting Continual Ultra-fine-grained Visual Recognition with Pre-trained Models"
authors:
  - "Pengcheng Zhang"
  - "Xiaohan Yu"
  - "Meiying Gu"
  - "Yuchen Wu"
  - "Yongsheng Gao"
  - "Xiao Bai"
doi: "10.24963/ijcai.2025/1053"
url: "https://ijcai.org/proceedings/2025/1053"
pdf: "https://ijcai.org/proceedings/2025/1053.pdf"
venue: "Proceedings of the Thirty-Fourth International Joint Conference on Artificial Intelligence"
year: 2025
pages: "9474-9482"
track: "AI4Tech: AI Enabling Technologies"
keywords:
  - "Domain-specific AI4Tech: AI4Agriculture"
---

# Revisiting Continual Ultra-fine-grained Visual Recognition with Pre-trained Models

Pengcheng Zhang, Xiaohan Yu, Meiying Gu, Yuchen Wu, Yongsheng Gao, Xiao Bai

*Proceedings of the Thirty-Fourth International Joint Conference on Artificial Intelligence*, pages 9474-9482. DOI: [10.24963/ijcai.2025/1053](https://doi.org/10.24963/ijcai.2025/1053)

## 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.