基于动态混淆发现的超细粒度图像分类网络

doi:10.16451/j.cnki.issn1003-6059.202605006

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Abstract In ultra-fine-grained visual classification(Ultra-FGVC), the inter-class differences are extremely subtle while the intra-class variations remain considerable. To address these issues, an ultra-fine-grained visual classification network based on dynamic confusion discovery(DCD-Net) is proposed. DCD-Net is composed of two key modules: the dynamic confusion discovery module(DCDM) and the confusion-aware dual contrastive learning module(CDCLM). DCDM constructs a confusion affinity matrix by analyzing the predicted probability distributions of the model. The globally optimal class pairing relationships are derived to identify the most confusable class pairs at the current training stage. CDCLM focuses on these identified confusion pairs to optimize the feature space from two perspectives: preserving intra-class feature consistency and enlarging the feature margin between easily confused classes. A collaborative mechanism is formed by the two modules through a confusion pairing table, enabling the network to dynamically adjust its learning focus throughout the training and continuously concentrate on the most indistinguishable class boundaries. Experiments on five ultra-fine-grained datasets and one fine-grained dataset demonstrate that DCD-Net achieves high recognition accuracy and strong generalization ability.

Key words： Ultra-Fine-Grained Visual Classification Contrastive Learning Dynamic Confusion Discovery Confusion Affinity Matrix Feature Space

Received: 26 March 2026

ZTFLH:

TP391.41

Fund:National Natural Science Foundation of China(No.62306151), Jiangsu Provincial Carbon Peak Carbon Neutrality Science and Technology Innovation Special Fund Project (No.BT2025029)

Corresponding Authors: LI Ang, Ph.D., lecturer. His research interests include computer vision.

About author:: WANG Yuliang, Master student. His research interests include image classification.
XIANG Sheng, Ph.D. candidate. His research interests include computer vision.
LU Wei, Master, lecturer. Her research interests include artificial intelligence.
WANG Xiaoming, Ph.D., associate professor. His research interests include wireless communications and artificial intelligence.

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Cite this article:

LI Ang,WANG Yuliang,XIANG Sheng等. Ultra-Fine-Grained Visual Classification Network Based on Dynamic Confusion Discovery[J]. Pattern Recognition and Artificial Intelligence, 2026, 39(5): 461-472.

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http://manu46.magtech.com.cn/Jweb_prai/EN/10.16451/j.cnki.issn1003-6059.202605006 OR http://manu46.magtech.com.cn/Jweb_prai/EN/Y2026/V39/I5/461

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